# Automating the analysis of public saliency and attitudes towards
biodiversity from digital media

Noah Giebink* Analytics Lab, Conservation Science Partners
0000-0003-0292-5472 Amrita Gupta* Analytics Lab, Conservation Science Partners
0000-0003-2643-5865 Diogo Veríssimo  On The Edge  0000-0001-6683-2078
Charlotte H. Chang  Department of Biology, Environmental Analysis Program,
Pomona College  0000-0002-1274-7721 Tony Chang  Analytics Lab, Conservation
Science Partners  0000-0001-6683-20781 Angela Brennan  Analytics Lab,
Conservation Science Partners  0000-0003-4360-0738 Brett Dickson  Analytics
Lab, Conservation Science Partners  0000-0003-4160-839X Alex Bowmer  On The
Edge  Jonathan Baillie  On The Edge

###### Abstract

Measuring public attitudes toward wildlife provides crucial insights into our
relationship with nature and helps monitor progress toward Global Biodiversity
Framework targets. Yet conducting such assessments at a global scale presents
challenges. Manual curation of search terms for querying mass media (news) and
social media is tedious and costly, and can lead to potentially biased
results. Raw news and social media data returned from queries are often
cluttered with irrelevant content and syndicated, or republished, articles. We
aim to overcome these challenges associated with monitoring public engagement
with biodiversity at scale by leveraging modern Natural Language Processing
(NLP) tools. We introduce a folk taxonomy approach for less biased and more
efficient search term generation. Additionally, we employ cosine similarity on
Term Frequency-Inverse Document Frequency vectors to identify and filter
syndicated articles. We introduce an extensible relevance filtering pipeline
which uses unsupervised learning to reveal common topics, followed by an open-
source zero-shot Large Language Model (LLM) to assign topics to news article
titles, which are then used to assign relevance. Finally, we conduct
sentiment, topic, and volume analyses on resulting data. To illustrate our
methodology, we conduct a case study of news and X (formerly Twitter) data
before and during the COVID-19 pandemic for various mammal taxa, including
bats, pangolins, elephants, and gorillas. During the data collection period,
up to 62% of articles mentioning bats were deemed irrelevant to biodiversity,
underscoring the importance of relevance filtering. At the pandemic’s onset,
we observed increased volume and a significant sentiment shift toward
horseshoe bats, which were implicated in the pandemic, but not for other focal
taxa. The proposed methods open the door to conservation practitioners
applying modern and emerging NLP tools, including LLMs “out of the box,” to
analyze public perceptions of biodiversity during current events or campaigns.

††* Equal contribution.††Correspondence to: Amrita Gupta
<EMAIL_ADDRESS>
Diogo Veríssimo<EMAIL_ADDRESS>
Charlotte Chang<EMAIL_ADDRESS>
_Keywords_ Conservation social science $\cdot$ Environmental social media
$\cdot$ Natural language processing

##  1 Introduction

Public interest in biodiversity is pivotal to the success of conservation
efforts, but varies significantly across species, geographies, and time. While
targeted conservation campaigns can amplify public engagement around focal
species and catalyze policy change [69], the systemic change needed to halt
biodiversity loss requires cultivating public awareness and support for nature
and biodiversity as a whole [46, 42].

Monitoring public attitudes towards species comprehensively and at scale is a
formidable challenge, but conservation culturomics–analyzing digital data to
examine societal relationships with nature–holds great promise for this
purpose [44, 60].

Digital data sources offer global reach and cost-efficiency over conventional
opinion-based surveys, and can reveal information-seeking behavior rather than
behavioral intent [43]. Although recent work has developed attention metrics
based on Wikipedia page views [63, 70] and Google Trends [43, 39, 70], news
and social media offer additional insights into the context of public
attention on species [66]. News media narratives shape public perceptions [57]
while social media have become a dominant platform for sharing news and
viewpoints toward issues including biodiversity conservation [41, 71, 64].
However, unlike Google Trends and Wikipedia page views, news and social media
yield unstructured text data, requiring careful search and filtering for
relevant content.

Selecting effective search terms for species in keyword-based search
application programming interfaces (APIs) is a nuanced task. This partly stems
from the mismatch between the specialized biological nomenclatures
conservation experts use, such as Latin (e.g., _Rhinolophus affinis_) or
specific common names (e.g., “Intermediate horseshoe bat”), and the broader
folk taxonomic terms the public favors (e.g., “bat” or “horseshoe bat”) that
may encompass multiple related species [38]. This highlights a trade-off
between specificity and volume of relevant content when assessing public views
on species groups. Using Latin [53, 61] or full common names [66, 59] as
keywords enhances specificity but risks overlooking general references to
species within folk taxonomies, potentially biasing search results towards
scientific content, especially for species lacking well-known common names.
Conversely, common names for folk taxa are challenging to infer. Past efforts
hand-curated common names for target taxa [49], but extending this approach to
thousands of species is both arduous and subjective. Additionally, some common
names (e.g., “elephant”) appear as substrings within unrelated species names
(e.g., “elephant seal”), requiring careful consideration when constructing
search queries.

Another challenge in conservation culturomics is the use of species common
names in non-biological contexts, such as sports teams (e.g., Clemson Tigers),
individuals (e.g., Tiger Woods), and other entities. Machine learning and
natural language processing (NLP) approaches can be used to develop text
classification models for filtering out such irrelevant results. These models
predict whether or not a sample of text pertains to biodiversity conservation
[59], target species or conservation topics [55, 51, 67, 47]. However, they
require extensive manually annotated data for training, are susceptible to
biases in data labeling, and may not generalize well to examples not seen
during training.

To address these challenges, we develop a pipeline for retrieving online news
and X (formerly Twitter) posts about biological taxa of conservation interest.
We introduce a novel method for deriving a folk taxonomy from English common
names via substring matching, simplifying the identification of names used in
everyday language to refer to animals. This approach facilitates analysis of
less well-known species by grouping them into more broadly recognized taxa,
overcoming the limitations posed by using only Latin or full common names for
these species. It also reveals spurious groupings of unrelated species,
corrected by incorporating negative search terms into API queries to enhance
search specificity. Furthermore, we use a zero-shot text classification model
to filter out irrelevant content, a cutting-edge machine learning approach
that obviates the need for data annotation by generalizing to new tasks
without additional training. We illustrate the utility of our pipeline in an
example analysis of public discourse on several mammal taxa from 2019 to 2021,
encompassing periods both before and after the United Nations World Health
Organization officially declared the COVID-19 pandemic on March 11, 2020.
Early in the outbreak, interest in wildlife increased, particularly in
potential zoonotic coronavirus sources like bats or pangolins [72, 65, 74]. We
explore changes in public perceptions toward bats and pangolins (versus
elephants and gorillas, which were not implicated in the pandemic) by
examining discourse volume and sentiment shifts over time.

##  2 Materials and Methods

Figure 1: A diagram of the data pipeline, starting from constructing a folk
taxonomy to derive search terms; retrieving news and tweets by querying each
data source; performing zero-shot relevance modeling and scraping to obtain
full-text for the news media articles; filtering out syndicated news and
identifying specific references to queried taxa within news articles; and
finally conducting analyses on shifts in volume, sentiment, and topics in the
tweets and news articles through time and over space.

Our pipeline for collecting online news articles and social media posts about
biological taxa of interest is illustrated in Figure 1 and summarized below:

  1. 1.

Query taxa and search term selection: We begin by selecting the taxa for
analysis, focusing on either individual species or broader categories based on
public visibility. This sets the foundation for our data collection by
specifying which species are encompassed in each targeted search. Details can
be found in Section 2.1.

  2. 2.

Information retrieval: Following the identification of target taxa and
corresponding search terms in the previous step, we use keyword search APIs to
retrieve online news articles and social media posts containing search terms
related to each query taxon (refer Section 2.2).

  3. 3.

Full text extraction: For news articles, where our initial retrieval yields
only titles and URLs, we first classify these article titles by topic to
determine their relevance to conservation (see Section 2.3.1). Only articles
deemed relevant undergo full text scraping (Section 2.3.2), ensuring
efficiency by avoiding the extraction of text from irrelevant articles.

  4. 4.

Data post-processing: We apply text similarity techniques to identify and
filter out syndicated articles, which are near-duplicates of original content
and could introduce redundancy into our text corpus. Further, we extract
specifically those sections of text with original articles that directly
reference the target taxa, thus enhancing the specificity of our analysis.

  5. 5.

Data and text analysis: We leverage the collected data for a range of analyses
aimed at uncovering insights into the public discourse surrounding the target
taxa. We explore the volume of online content about different target taxa and
how that varies geographically and over time. Sentiment analysis can help
track shifts in the tone of these discussions, while topic analysis sheds
light on underlying themes in these discussions. These examples illustrate the
versatility of our dataset in facilitating diverse analytical approaches to
deepen our understanding of the discourse dynamics related to the target taxa.

###  2.1 Search term selection

Figure 2: Example of an initial connected component in the folk taxonomy graph
for species in Order Carnivora based on their IUCN Red List common names.
Solid lines represent edges between species and their listed common names;
dashed lines represent edges between names and simplified names; and dotted
lines represent connections that would be pruned on inspection to separate
conceptually distinct taxa.

Identifying salient folk taxa–groups of species as referenced in everyday
language–is a fundamental step in monitoring public perceptions of these taxa
in conservation contexts. We accomplished this through a human-in-the-loop
approach, using English-language common names for species and their simplified
forms as the basis for identifying these taxa in our analysis. First, we
gathered the comprehensive list of mammalian species and their English common
names from the International Union for Conservation of Nature and Natural
Resources (“IUCN”) Red List (IUCNredlist.org), encompassing a total of 5,650
species and 9,150 common names. We leveraged an efficient dynamic programming
algorithm to extract shared trailing substrings from the common names (such as
"sea lion" from "South American sea lion" and "Californian sea lion", see Fig.
2), yielding prospective folk taxa. We then constructed an undirected graph
representation of the connections between species, their common names, and the
identified shared substrings. We clustered this graph into connected
components, each of which represents a candidate taxon comprised of a group of
species and a simplified set of names for them. Each cluster was manually
inspected to ensure that the species formed a coherent group. Otherwise, nodes
or edges in the graph were modified before repeating the clustering and
inspection.

In some cases, our method grouped several taxa into a broader taxon that might
be considered too coarse. For instance, the “Andean bear”, “black bear”,
“brown bear”, “polar bear”, “sloth bear”, and “sun bear” were initially
grouped under the “bear” taxon. Given the widespread recognition of distinct
bear species, one could consider eliminating the node associated with the
shared substring “bear” to separate these species into distinct clusters. A
more complex issue arises, however, when shared substrings are found between
common names of unrelated species. For instance, the substring “lion” appears
in the common names for _Panthera leo_ (“lion”), _Puma concolor_ (“mountain
lion”), _Leontopithecus spp._ (“lion tamarins”), _Zalophus wollebaeki_
(“Galápagos sea lion”), and other unrelated species (Fig. 2). Conducting a
search using the term “lion” could potentially yield results encompassing all
these taxa. To avoid this, we incorporated negative keywords (e.g. “lion” AND
NOT “mountain lion” AND NOT “sea lion” AND NOT “lion tamarin”) to improve
differentiation among these species during searches, a strategy not documented
in prior work.

For each folk taxonomic entity, we compiled a set of positive keywords, at
least one of which must be present in a search result, and an optional set of
negative keywords, all of which must be absent from a search result.
Additional details about the graph construction can be found in Supplementary
Information Section A.1.

###  2.2 News and social media information retrieval

We collected online news articles from the Global Database of Events,
Language, and Tone (GDELT), a live database capturing global news media
offering full-text search via the GDELT 2.0 DOC API. Using positive and, where
applicable, negative search keywords for each target taxon, we requested
English-language articles published between January 1, 2019 and December 31,
2021. Each query returned JSON-formatted article metadata that included the
article’s title, URL, domain, date, and country of publication. To work within
the limit of 250 results per query imposed by the API, we divided the three
year period into shorter intervals, aggregating results from each interval to
form our final dataset.

Similarly, for social media analysis, we utilized the Twitter Academic Access
v2 API to access Twitter’s full archive of public tweets. We queried this API
with the positive and negative keywords for each target folk taxonomic entity,
requesting only tweets written in English and including geolocation data to
support analyses on geographic differences in species media portrayals.
Twitter data collection concluded before February 9, 2023, ahead of potential
deprecation notices for the Academic Access API by Twitter.

###  2.3 News full-text extraction

####  2.3.1 Relevance filtering

The keyword-based search described above often retrieves a mixture of relevant
and irrelevant results for wildlife conservation [59]. For instance, a query
using the search term “tiger” might fetch articles mentioning sports teams
(e.g. the Clemson Tigers), people (e.g. Tiger Woods or Tiger Shroff),
companies (e.g. Tiger Global Management, LLC), places (e.g. Tiger Hill), or
even events (e.g. Year of the Tiger). Articles in which the search keywords
refer to non-animal entities should be excluded from the corpus of wildlife-
focused news articles. However, GDELT queries return only metadata such as
titles and URLs, not full texts, requiring us to decide whether an article
likely uses the search keywords in the intended sense from these relatively
limited metadata.

We make the simplifying assumption that articles about topics related to
nature and conservation are more likely to use keywords in the intended
context. Our goal, then, is to classify the title of a news article as
relevant or irrelevant to wildlife or conservation. We developed a topic
classification-based approach, in which an online news article is predicted as
belonging to one or more predefined topics, a subset of which are considered
relevant. We derived the set of predefined article topics in a two-stage
approach. In the first stage, we randomly sampled 10,000 articles from GDELT
query results for news articles from 2019, stratified such that at least one
taxon from each of 14 Mammalian Orders was represented. The resulting sample
contained mentions of 154 mammalian taxa. The full-text of these articles was
obtained via webscraping (see Section 2.3.2) and text snippets containing
animal search terms were extracted, with each snippet being 7 sentences long
(for context, 3 sentences before and after the sentence mentioning the taxon).
We used Latent Dirichlet Allocation (LDA) to perform unsupervised topic
modeling on these text snippets, obtaining 40 initial topics. LDA models texts
as mixtures of topics, which are themselves mixtures of words, allowing for
the discovery of underlying thematic structures in large text corpora. The
choice of 40 topics was numerous enough to glean many informative topics
without exceeding the model’s capacity to reliably converge within 150
iterations. We reviewed the 20 words scored most important by the model for
each topic to assign a semantically meaningful label to each one, yielding 23
topic labels which we then grouped into relevant versus irrelevant topics as
follows:

Relevant: agriculture, climate change, conservation, energy, health,
infrastructure, natural disasters, nature, outdoor recreation, science and
technology, tourism, wildlife, habitat loss, invasive species, pollution

Irrelevant: business, crime, education, entertainment, food, holidays,
politics, sports

We defined relevant topics as those that discussed species in a biological,
conservation, or real-world context, whereas irrelevant topics were instead
focused on non-biological issues.

Given these predefined topics, our next challenge was to classify GDELT query
results among these predefined topics, keeping in mind that we have access to
only the article title at this stage in the GDELT data collection pipeline. We
used Facebook’s Bidirectional and Autoregressive Transformers (“BART”) model
to perform multi-label “zero-shot” topic classification for the GDELT article
data using the topics identified through our LDA analysis of the full-text
subset dataset [62]. Each article title received a unit-sum vector of topics
with probabilities across the 23 topics enumerated above. If an article title
was predicted as having any of the relevant topics with a model score greater
than 0.5, the article was considered relevant and was flagged for webscraping.
The zero-shot BART model is capable of predicting topics on new data, given
the extensive scale of the data that were used to train these models and their
watershed advance in creating numeric representations (also known as
“embeddings”) that can capture the semantic structure of the English language.
The major advantage of these models is that they enable conservation
practitioners to now filter text corpora that would simply be impossible to
manually review.

####  2.3.2 Scraping the full text of articles

To obtain the full text of news articles flagged as relevant, we first
submitted an HTTP request for the HTML content of each relevant GDELT news
article URL. If the request was successful, the HTML content was parsed using
one of three Python libraries (trafilatura, newsplease, or boilerpy3) to
extract the article body. Often, however, the HTML request or the text
extraction was unsuccessful due to broken URLs. As a method of recourse in
these cases, we searched for a snapshot of the article on the Internet
Archive. If a snapshot was found, we requested the HTML content of this
snapshot and attempted to extract the article body text using the same
combination of Python libraries as before.

###  2.4 News data post-processing

In mainstream media, news articles are often syndicated across multiple
outlets with minimal changes to the text [59]. To prevent bias in downstream
models and avoid redundant analyses on near-identical content, we implemented
a process to identify duplicates. We measured the similarity between articles
by first using Term Frequency-Inverse Document Frequency to create a vector
representation of each article’s text based on its most distinctive words, and
then computing the cosine similarity between pairs of article vectors. We
compared all pairs of articles published within two months of each other, as
syndicated articles are typically released soon after their originals. If the
cosine similarity exceeded 0.95, indicating a high degree of similarity, we
classified the later-published article as a syndicate of the earlier one.
Conversely, if an article’s cosine similarity score with every other article
published within the preceding two months was below 0.95, we classified it as
an original.

Next, we isolated sentences within articles that directly reference the target
taxa, a step we call “entity mention detection”. This step enables us to
precisely apply NLP tasks like sentiment analysis and topic modeling to text
segments containing the entity of interest. This is especially useful for
longer bodies of text like articles, which can discuss many different things
and have shifts in tone. We scanned each article for the positive search terms
for a target taxon. Upon finding a mention, we extract the sentence that
contains this reference along with the sentence immediately preceding it.
Including the preceding sentence is helpful as it often frames the mention
with additional context.

###  2.5 Analyzing public discourse about species

For each taxon, we had a set of articles and tweets from 2019 through the end
of 2021. To determine the overall volume of discourse toward each taxon, we
aggregated the number of articles mentioning each taxon by month and country.

We examined the sentiment of media and public discussion of species using a
lexicon sentiment model. Specifically, we used the “Valence Aware Dictionary
and sEntiment Reasoner” (abbreviated to VADER, [52]). This yielded a sentiment
score for each article ranging from -1 (negative) to 0 (neutral) to 1
(positive). We also aggregated these article-level sentiment scores by month
and country to examine patterns in public discourse regarding species.

We illustrate how conservation social science researchers and practitioners
interested in messaging or marketing to conserve biodiversity can perform
different analyses using the outputs of our data pipeline. Using information
on the country where each news article is published, we show how one can
create choropleth maps of the volume of public discourse toward different
taxa. We use chord diagrams to visualize the distribution and co-occurrence of
different topics associated with news media coverage of each taxa. Finally, we
use breakpoint analyses [56] to evaluate whether or not there were significant
changes in the mean volume or sentiment through time for different taxa.

##  3 Results

###  3.1 Creating a folk taxonomy and collecting data on target taxa

Figure 3: Number of news articles obtained at each stages in the GDELT data
collection pipeline run fully on ten query taxa, from querying, to relevance
filtering, webscraping, and deduplication.

Using the approach described in Section 2.1, we derived folk taxonomic terms
for the species within each of the 26 Orders of mammals in the IUCN Red List.
Figure 2 illustrates one of the connected components for the Order Carnivora
before inspection, conveniently grouping several species under the folk taxon
“sea lion”–a term the public is more likely to use than any of those species’
full common names. However, it also reveals links between ‘lion’ and both ‘sea
lion’ and ‘mountain lion’ due to the shared substring ‘lion.’ These links,
which could lead to mixed search results when searching for ’lion,’
highlighted the need for negative search terms to improve search specificity.
We conducted comprehensive analysis using our proposed pipeline for 10 taxa,
which are listed along with their corresponding scientific taxa in Table A1.
These range from the Genus level (gorilla) to the Order level (bats). We also
considered more specific yet popularly recognized taxa like “flying fox” and
“vampire bat” and lesser-known but still distinct taxa like “pipistrelle” and
“horseshoe bat”.

Figure 3 shows the outcome at each stage of our GDELT data collection pipeline
applied to the 10 case study taxa. Raw article counts varied from 588,077
results for “bat” to 311 for “long-tongued bat”. Notably, approximately 54% of
articles were predicted to be irrelevant to wildlife, with “bat” (62.6%),
“gorilla” (48.4%), “elephant” (45.3%) and “vampire bat” (36.4%) yielding high
proportions of unrelated content due to homonymy (e.g. “bat” as a piece of
sports equipment), idiomatic expressions (e.g. “elephant in the room”, “800
pound gorilla”, and “off the bat”), and popular culture depictions of these
animals. Full-text scraping was attempted for all articles that were predicted
to be relevant based on their title, yet about a third were inaccessible due
to broken links. We also found that 41% of articles across these taxa were
syndicated, indicating significant potential for computational efficiency by
limiting analyses to only relevant, original articles (Table A2). Ultimately,
taxa with widespread popular appeal (elephants, gorillas) had more wildlife
news articles than lesser-known taxa (pangolins), and generic taxa had more
articles than specific ones.

Pivoting to social media, we observed that the public made anywhere between
several hundred to nearly 300,000 posts about different taxa from 2019 to 2022
(Table 1). In our subsequent analyses, we now illustrate different use cases
of the data generated by our data pipeline.

Table 1: The counts across taxa for Twitter posts from 2019-2021. Entity | Count
---|---
Elephant | 171059
Gorilla | 52650
Pangolin | 3667
Bat | 325319
Flying fox | 1889
Myotis | 166
Horseshoe bat | 140
Pipistrelle | 354
Vampire bat | 462
Long-tongued bat | 80

###  3.2 How does discourse vary around the world?

((a)) Gorilla

((b)) Pangolin

((c)) Pipistrelle

Figure 4: Volume of relevant news articles from GDELT (left) and Tweets
(right) between January 1, 2019 and December 31, 2022 for gorillas (4(a)),
pangolins (4(b)), and pipistrelles (4(c)). Insets show top 10 countries by
volume.

Figure 4 shows spatial variations in media coverage across different taxa.
Globally recognized animals like gorillas receive widespread attention online,
while less well-known taxa such as pangolins and pipistrelle bats see more
geographically concentrated coverage. Pangolins are primarily featured in
Southeast Asia, whereas pipistrelle bats, despite their prevalence in the
British Isles and widespread distribution in Asia, attract less media
attention outside the UK. These findings show media exposure to different
animal taxa varies by geography, potentially influencing levels of awareness
and familiarity.

###  3.3 Which topics are associated with different taxa?

The advent of large language models trained on Internet-scale text corpora
offers major advances for zero-shot learning, where practitioners can use the
predictions of a model on their own datasets to help sift through volumes of
data that simply defy manual review. Figure 5 displays the co-occurrence of
topics predicted by Facebook’s BART (Bidirectional and Auto-Regressive
Transformers) model [62]. Each line in one of the chord diagrams represents a
topic that is co-occurring in an article with another topic (for the full set
of chord diagrams for all folk taxonomic entities, please refer to Figure 8).

Figure 5: Chord diagrams depicting the co-occurrence of relevant topics for
two focal taxa, Horseshoe bat (family Rhinolophidae) and Long-tongued bat
(genera Glossophaga, Craseonycteris, and Leptonycteris). A wider chord
indicates that more articles contain two taxa and each chord is a band colored
by one of the nodes that is being connected. The circular perimeter of the
chord displays the proportional occurrence of each topic in the dataset, and
the colors correspond to different groups of topics.

Practitioners could use approaches such as these to evaluate how wildlife is
framed in the news media. Comparing horseshoe bats, a known reservoir of SARS-
CoV, versus long-tongued bats, which are not regarded as a coronavirus
reservoir, we observed that the distribution and co-occurrence of topics was
quite different between these two groups of species. Long-tongued bats had
much more news coverage devoted to nature-based topics such as conservation or
wildlife. Moreover, the nature or conservation threats topics (e.g. nature,
wildlife, climate change, habitat loss, etc.) tend to occur with one another
in articles. In contrast, horseshoe bat media coverage exhibited comparatively
more discourse on the topics of conservation threats (e.g. habitat loss,
natural disasters, climate change) or socio-economic issues (e.g. business,
health, education). Health and food were more prevalent topics for horseshoe
bats compared to long-tongued bats. For both types of bat, however, the chord
diagram indicates that there is substantial co-occurrence of different topics
at the level of individual articles.

###  3.4 How has the salience of taxa changed through time?

Figure 6 displays changes in the volume of mass media articles referencing
different taxa, shown using normalized counts for each taxa’s count of
articles or Twitter posts, aggregated over a two week sliding window.
Comparing taxa implicated as coronavirus hosts or as potential spillover hosts
(pangolin or horseshoe bat) versus species of conservation concern that are
not clearly associated with COVID-19 (e.g. elephant), we observed differences
in the salience of these taxa.

Figure 6: Changes in volume through time. The solid vertical magenta line
denotes March 11, 2020, which was the date when the UN WHO declared COVID-19 a
pandemic. The dashed vertical orange or blue lines correspond to any
significant breakpoints in the trend for GDELT or Twitter respectively, after
conducting Bonferroni family-wise error correction.

A breakpoint analysis indicated that there were significant changes in the
volume of news media articles published on horseshoe bats. Specifically, there
were an average of 3 articles published every two weeks on horseshoe bat
before January 10, 2020, and this volume jumped to an average of 20 articles
every two weeks after this breakpoint. We did not observe any other
significant breakpoints for the count of Twitter posts or the count of news
media articles for any of the other focal taxa. However, in the full set of
taxa (Figure 9), we found breakpoints in news media coverage for flying fox,
gorilla, and myotis between November 2019 to September 2020. We did not find
any breakpoints for any taxa in terms of the volume of Twitter posts in our
sample. However, unlike the horsehoe bat, all of these taxa exhibited either
no change or a reduction in average volume in news media content.

###  3.5 How has the sentiment of discourse about taxa changed through time?

Practitioners and researchers may also seek to monitor changes in public
sentiment toward different taxa. We illustrate how such analyses can be
conducted with the outputs of our pipeline. Focusing on the same focal taxa of
elephant, horseshoe bat, and pangolin, we compare and contrast changes in mean
article or Twitter post sentiment, measured through a unidimensional value
ranging from -1 (very negative) to 0 (neutral) to 1 (very positive) (Figure
7). We saw that the mean sentiment toward taxa was lowest for pangolins in the
news, with a mean of -0.01, and highest for long-tongued bat on Twitter, with
a mean of 0.28. Of the three focal species, pangolins on the news had the
lowest average sentiment, but higher sentiment on Twitter (0.14), elephants
had an average sentiment of 0.12 (news) or 0.13 (Twitter), and horseshoe bats
had the highest average sentiment across the board (0.18 in the news and 0.26
on Twitter).

Across all of the taxa in our pipeline, we only observed a significant
breakpoint in sentiment for horseshoe bat (Figure 10). Horseshoe bat discourse
showed a change in sentiment on October 6, 2020 across both news media
coverage and Twitter posts. The mean sentiment of horseshoe bat coverage
remained the same (0.2 on a scale from -1 to +1) in the news media; however,
Twitter horseshoe bat sentiment changed from an average of 0.2 to 0.4,
becoming more positive through time.

Figure 7: Changes in sentiment through time. The solid vertical magenta line
denotes March 11, 2020, which was the date when the UN WHO declared COVID19 a
pandemic. The dashed vertical orange or blue lines correspond to any
significant breakpoints in the trend for GDELT or Twitter respectively, after
conducting Bonferroni family-wise error correction.

##  4 Discussion

Our data pipeline permits practitioners and researchers to monitor public
perceptions of biodiversity globally and with geographic or temporal
disaggregation. This project builds on several recent advances using NLP
machine learning approaches to process and analyze large, unstructured text
data about biodiversity. For instance, [59] created a pipeline to detect and
extract news articles mentioning more than 500 CITES Appendix 1 species in the
news media. [47] analyzed articles from the Times of India for instances of
human-wildlife conflict in West Bengal with 15 species, such as the Asian
elephant (Elephas maximas). In our project, we extended these bodies of work
by simultaneously scraping data from the news media and social media, by
creating a folk taxonomy to broaden the data sampled, by leveraging cutting-
edge large language models to filter our data in an efficient, performant, and
replicable fashion, and by scraping the Internet Archive, which permits us to
mitigate issues such as the ephemerality of digital media.

One advance of our approach is using approaches drawn from string algorithms
and graph theory to generate a folk taxonomy. We showed examples of how
researchers and practitioners can develop a folk taxonomy to identify unique
common names for sets of species. Such a folk taxonomy can permit future
monitoring efforts to capture more of the potentially relevant discourse
toward biodiversity [38]. At the same time, using a folk taxonomy increases
the volume of results at the expense of introducing some quantity of non-
relevant content; we demonstrate how zero-shot LLMs can deal with this
problem.

Zero-shot approaches allow conservationists to judiciously and efficiently
filter public content about biodiversity using cutting-edge machine learning
models “out of the box”. Therefore, conservation practitioners and researchers
do not need to invest resources in creating labelled training and test data, a
necessity for training a model from the ground up or fine-tuning an existing
model using transfer learning, which may not always be feasible or advisable,
especially for complex models with millions of parameters. In our case, we
found that up to 62% of the articles about bats were irrelevant, showing the
importance of filtering out results that are not related to public perceptions
of nature. The choropleth maps gesture toward the relative popularity of
different folk taxa; we observed that there was much broader coverage and
higher volume in general for prominent taxa such as elephants or more
generalized entities such as bat across Twitter and news media.

Using the data generated from our pipeline, we saw that taxa differed in the
distribution of topics in news media articles, and in terms of their volume
and sentiment through time in the news media or on Twitter. We saw that the
volume of news articles about horseshoe bats increased during the early days
of the COVID19 outbreak, and that this contrasted with the other bat taxa in
our pipeline, which largely did not exhibit any significant changes in volume.
In contrast, while sentiment toward horseshoe bats was generally positive, our
results indicated that there was a significant breakpoint in sentiment for
both Twitter and news media horseshoe bat discourse in late 2020. These types
of analyses can be extended in future monitoring and research efforts to
evaluate the impact of public campaigns to conserve biodiversity or monitor
human-nature perceptions in general [48, 50, 63, 73, 44]. Changes in the
volume of discourse about species can herald problems such as the societal
extinction of rare species [54]. Calculating metrics such as volume and
sentiment from automated data tracking public perceptions of biodiversity
offers new, standardized ways to monitor public interest in biodiversity more
broadly [45].

Our approach can serve as the foundation for an automated “nature tracker”,
which would permit practitioners and researchers to track public perceptions
of biodiversity. Monitoring human-nature perceptions is critical to evaluating
progress toward the targets of the Global Biodiversity Framework, particularly
the targets focused on human-wildlife conflict and sustainable use. By
scraping and processing data from the news and social media, we can provide
real-time, cost-effective insight that is global in scale. Therefore, digital
approaches open new avenues for assessing compliance with the Global
Biodiversity Framework, which is particularly pressing given that even as late
as 2024 most of these targets still show significant gaps in their evaluation
mechanisms. Without effective resolution that enables tracking over
substantial time periods, these monitoring deficiencies could render the
targets politically irrelevant, as it would be impossible to evaluate the
progress–or lack thereof–made by different countries.

In considering the future development of our data pipeline, we have identified
several key areas for future exploration and enhancement. A primary aspect to
address revolves around the linguistic scope of our approach, which currently
centers solely on English-language data. It will be key for future work to
broaden this scope to include other languages spoken in megadiverse countries,
such as Spanish, Chinese, Portuguese or Bahasa Indonesia. Furthermore, it is
evident that our conservation social science monitoring must adapt to dynamic
shifts in platform governance and data accessibility. Recent transitions in
the ownership and management of platforms like Twitter have underscored the
urgency of this need. These transitions have coincided with the proliferation
of misinformation regarding climate change [58] and wildlife in the context of
the COVID-19 pandemic and a marked decline in active users, particularly
environmentally-focused users [68, 40], both of which pose increasing
challenges to monitoring approaches using online data.

Overall, this study highlights the potential benefits of combining machine
learning with the automated tracking of different data platforms to monitor
public perceptions of biodiversity. We anticipate that methods such as ours or
building on our approach can enhance applied conservation by creating new ways
to examine human-nature perceptions at a global scale.

##  5 Acknowledgments

We express our gratitude to the institutions that supported the work presented
in this paper, which was conducted while the authors were affiliated as listed
in the manuscript. We note that several authors have since moved to new
affiliations: Tony Chang is now with Vibrant Planet, Amrita Gupta is at the
Microsoft AI for Good Lab, Diogo Verissimo is currently at the University of
Oxford, and Noah Giebink is with the Spatial Informatics Group. These changes
in affiliation are mentioned for the sake of accuracy regarding the authors’
current positions.

## References

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  * [2] Zuzana Burivalova, Rhett A Butler and David S Wilcove  “Analyzing Google Search Data to Debunk Myths about the Public’s Interest in Conservation”  In _Frontiers in Ecology and the Environment_ 16.9, 2018, pp. 509–514
  * [3] Charlotte H Chang, Yuta J Masuda and Paul R Armsworth  “Environmental Discourse Exhibits Consistency and Variation across Spatial Scales on Twitter”  In _BioScience_ 72.8, 2022, pp. 789–797
  * [4] Charlotte H Chang, Nikhil R Deshmukh, Paul R Armsworth and Yuta J Masuda  “Environmental users abandoned Twitter after Musk takeover”  In _Trends in Ecology & Evolution_ Elsevier, 2023
  * [5] Convention on Biological Diversity  “Kunming-Montreal Global Biodiversity Framework”, 2022  URL: https://www.cbd.int/doc/decisions/cop-15/cop-15-dec-04-en.pdf
  * [6] Matthew W. Cooper et al.  “Developing a Global Indicator for Aichi Target 1 by Merging Online Data Sources to Measure Biodiversity Awareness and Engagement”  In _Biological Conservation_ 230, 2019, pp. 29–36
  * [7] Ricardo A. Correia et al.  “Digital Data Sources and Methods for Conservation Culturomics”  In _Conservation Biology_ 35.2, 2021, pp. 398–411
  * [8] Gabriel Henrique de Oliveira Caetano et al.  “Evaluating Global Interest in Biodiversity and Conservation”  In _Conservation Biology_ , 2022
  * [9] Sandra Díaz et al.  “Pervasive Human-Driven Decline of Life on Earth Points to the Need for Transformative Change”  In _Science_ 366.6471 American Association for the Advancement of Science, 2019, pp. eaax3100
  * [10] Gokhan Egri et al.  “Detecting Hotspots of Human-Wildlife Conflicts in India Using News Articles and Aerial Images”  In _ACM SIGCAS/SIGCHI Conference on Computing and Sustainable Societies (COMPASS)_ Seattle WA USA: ACM, 2022, pp. 375–385
  * [11] Darío Fernández-Bellon and Adam Kane  “Natural History Films Raise Species Awareness—A Big Data Approach”  In _Conservation Letters_ 13.1 Wiley Online Library, 2020, pp. e12678
  * [12] Christoph Fink, Anna Hausmann and Enrico Di Minin  “Online Sentiment towards Iconic Species”  In _Biological Conservation_ 241, 2020, pp. 108289
  * [13] Niall L. Hammond, Amy Dickman and Duan Biggs  “Examining Attention given to Threats to Elephant Conservation on Social Media”  In _Conservation Science and Practice_ 4.10, 2022, pp. e12785
  * [14] Sara Bronwen Hunter, Fiona Mathews and Julie Weeds  “Using hierarchical text classification to investigate the utility of machine learning in automating online analyses of wildlife exploitation”  In _Ecological Informatics_ 75 Elsevier, 2023, pp. 102076
  * [15] Clayton Hutto and Eric Gilbert  “Vader: A Parsimonious Rule-Based Model for Sentiment Analysis of Social Media Text”  In _Proceedings of the International AAAI Conference on Web and Social Media_ 8, 2014, pp. 216–225
  * [16] Ivan Jarić et al.  “Societal Attention toward Extinction Threats: A Comparison between Climate Change and Biological Invasions”  In _Scientific Reports_ 10.1 Nature Publishing Group, 2020, pp. 11085
  * [17] Ivan Jarić et al.  “Societal Extinction of Species”  In _Trends in Ecology & Evolution_ 37.5, 2022, pp. 411–419
  * [18] Sedrick Scott Keh et al.  “NewsPanda: Media Monitoring for Timely Conservation Action”  In _Proceedings of the AAAI Conference on Artificial Intelligence_ 37.13, 2023, pp. 15528–15536  DOI: 10.1609/aaai.v37i13.26841
  * [19] Rebecca Killick and Idris A. Eckley  “changepoint: An R Package for Changepoint Analysis”  In _Journal of Statistical Software_ 58.3, 2014, pp. 1–19
  * [20] Gary King, Benjamin Schneer and Ariel White  “How the News Media Activate Public Expression and Influence National Agendas”  In _Science (New York, N.Y.)_ 358.6364 American Association for the Advancement of Science, 2017, pp. 776–780
  * [21] Jennie King  “Deny, Deceive, Delay Vol. 2: Exposing New Trends in Climate Mis- and Disinformation at COP27”, 2023
  * [22] Ritwik Kulkarni and Enrico Di Minin  “Automated Retrieval of Information on Threatened Species from Online Sources Using Machine Learning”  In _Methods in Ecology and Evolution_ 12.7, 2021, pp. 1226–1239
  * [23] Richard J Ladle et al.  “Conservation Culturomics”  In _Frontiers in Ecology and the Environment_ 14.5, 2016, pp. 269–275
  * [24] Richard J Ladle, Paul Jepson, Ricardo A Correia and Ana CM Malhado  “A culturomics approach to quantifying the salience of species on the global internet”  In _People Nat_ 1.4, 2019, pp. 524–532
  * [25] Mike Lewis et al.  “BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension”  arXiv, 2019  arXiv:1910.13461 [cs, stat]
  * [26] Joseph W. Millard, Richard D. Gregory, Kate E. Jones and Robin Freeman  “The Species Awareness Index as a Conservation Culturomics Metric for Public Biodiversity Awareness”  In _Conservation Biology_ 35.2, 2021, pp. 472–482
  * [27] S.k. Papworth et al.  “Quantifying the role of online news in linking conservation research to Facebook and Twitter”  In _Conservation Biology_ 29.3, 2015, pp. 825–833
  * [28] Silviu O. Petrovan et al.  “Post COVID-19: A Solution Scan of Options for Preventing Future Zoonotic Epidemics”  In _Biological Reviews_ 96.6, 2021, pp. 2694–2715
  * [29] Jean-Michel Roberge  “Using Data from Online Social Networks in Conservation Science: Which Species Engage People the Most on Twitter?”  In _Biodiversity and Conservation_ 23.3, 2014, pp. 715–726
  * [30] Uri Roll, Ricardo A Correia and Oded Berger-Tal  “Using machine learning to disentangle homonyms in large text corpora”  In _Conservation Biology_ 32.3 Wiley Online Library, 2018, pp. 716–724
  * [31] Chris Stokel-Walker  “Twitter May Have Lost More than a Million Users since Elon Musk Took Over”, 2022
  * [32] Andrew D Thaler, Naomi A Rose, A Mel Cosentino and Andrew J Wright  “Lions, whales, and the web: transforming moment inertia into conservation action”  In _Frontiers in Marine Science_ 4 Frontiers Media SA, 2017, pp. 292
  * [33] Reut Vardi, John C. Mittermeier and Uri Roll  “Combining Culturomic Sources to Uncover Trends in Popularity and Seasonal Interest in Plants”  In _Conservation Biology_ 35.2, 2021, pp. 460–471
  * [34] Diogo Veríssimo  “Trends in Digital Marketing for Biodiversity Conservation”, 2021
  * [35] Varsha Vijay, Christopher R. Field, Florian Gollnow and Kelly K. Jones  “Using Internet Search Data to Understand Information Seeking Behavior for Health and Conservation Topics during the COVID-19 Pandemic”  In _Biological Conservation_ 257, 2021, pp. 109078
  * [36] Joss Wright, Robert Lennox and Diogo Veríssimo  “Online Monitoring of Global Attitudes Towards Wildlife”  arXiv, 2020  arXiv:2007.11506 [cs]
  * [37] Peng Zhou et al.  “A Pneumonia Outbreak Associated with a New Coronavirus of Probable Bat Origin”  In _nature_ 579.7798 Nature Publishing Group, 2020, pp. 270–273

## References

  * [38] Anne H. Beaudreau, Phillip S. Levin and Karma C. Norman  “Using Folk Taxonomies to Understand Stakeholder Perceptions for Species Conservation”  In _Conservation Letters_ 4.6, 2011, pp. 451–463
  * [39] Zuzana Burivalova, Rhett A Butler and David S Wilcove  “Analyzing Google Search Data to Debunk Myths about the Public’s Interest in Conservation”  In _Frontiers in Ecology and the Environment_ 16.9, 2018, pp. 509–514
  * [40] Charlotte H Chang, Nikhil R Deshmukh, Paul R Armsworth and Yuta J Masuda  “Environmental users abandoned Twitter after Musk takeover”  In _Trends in Ecology & Evolution_ Elsevier, 2023
  * [41] Charlotte H Chang, Yuta J Masuda and Paul R Armsworth  “Environmental Discourse Exhibits Consistency and Variation across Spatial Scales on Twitter”  In _BioScience_ 72.8, 2022, pp. 789–797
  * [42] Convention on Biological Diversity  “Kunming-Montreal Global Biodiversity Framework”, 2022  URL: https://www.cbd.int/doc/decisions/cop-15/cop-15-dec-04-en.pdf
  * [43] Matthew W. Cooper et al.  “Developing a Global Indicator for Aichi Target 1 by Merging Online Data Sources to Measure Biodiversity Awareness and Engagement”  In _Biological Conservation_ 230, 2019, pp. 29–36
  * [44] Ricardo A. Correia et al.  “Digital Data Sources and Methods for Conservation Culturomics”  In _Conservation Biology_ 35.2, 2021, pp. 398–411
  * [45] Gabriel Henrique de Oliveira Caetano et al.  “Evaluating Global Interest in Biodiversity and Conservation”  In _Conservation Biology_ , 2022
  * [46] Sandra Díaz et al.  “Pervasive Human-Driven Decline of Life on Earth Points to the Need for Transformative Change”  In _Science_ 366.6471 American Association for the Advancement of Science, 2019, pp. eaax3100
  * [47] Gokhan Egri et al.  “Detecting Hotspots of Human-Wildlife Conflicts in India Using News Articles and Aerial Images”  In _ACM SIGCAS/SIGCHI Conference on Computing and Sustainable Societies (COMPASS)_ Seattle WA USA: ACM, 2022, pp. 375–385
  * [48] Darío Fernández-Bellon and Adam Kane  “Natural History Films Raise Species Awareness—A Big Data Approach”  In _Conservation Letters_ 13.1 Wiley Online Library, 2020, pp. e12678
  * [49] Christoph Fink, Anna Hausmann and Enrico Di Minin  “Online Sentiment towards Iconic Species”  In _Biological Conservation_ 241, 2020, pp. 108289
  * [50] Niall L. Hammond, Amy Dickman and Duan Biggs  “Examining Attention given to Threats to Elephant Conservation on Social Media”  In _Conservation Science and Practice_ 4.10, 2022, pp. e12785
  * [51] Sara Bronwen Hunter, Fiona Mathews and Julie Weeds  “Using hierarchical text classification to investigate the utility of machine learning in automating online analyses of wildlife exploitation”  In _Ecological Informatics_ 75 Elsevier, 2023, pp. 102076
  * [52] Clayton Hutto and Eric Gilbert  “Vader: A Parsimonious Rule-Based Model for Sentiment Analysis of Social Media Text”  In _Proceedings of the International AAAI Conference on Web and Social Media_ 8, 2014, pp. 216–225
  * [53] Ivan Jarić et al.  “Societal Attention toward Extinction Threats: A Comparison between Climate Change and Biological Invasions”  In _Scientific Reports_ 10.1 Nature Publishing Group, 2020, pp. 11085
  * [54] Ivan Jarić et al.  “Societal Extinction of Species”  In _Trends in Ecology & Evolution_ 37.5, 2022, pp. 411–419
  * [55] Sedrick Scott Keh et al.  “NewsPanda: Media Monitoring for Timely Conservation Action”  In _Proceedings of the AAAI Conference on Artificial Intelligence_ 37.13, 2023, pp. 15528–15536  DOI: 10.1609/aaai.v37i13.26841
  * [56] Rebecca Killick and Idris A. Eckley  “changepoint: An R Package for Changepoint Analysis”  In _Journal of Statistical Software_ 58.3, 2014, pp. 1–19
  * [57] Gary King, Benjamin Schneer and Ariel White  “How the News Media Activate Public Expression and Influence National Agendas”  In _Science (New York, N.Y.)_ 358.6364 American Association for the Advancement of Science, 2017, pp. 776–780
  * [58] Jennie King  “Deny, Deceive, Delay Vol. 2: Exposing New Trends in Climate Mis- and Disinformation at COP27”, 2023
  * [59] Ritwik Kulkarni and Enrico Di Minin  “Automated Retrieval of Information on Threatened Species from Online Sources Using Machine Learning”  In _Methods in Ecology and Evolution_ 12.7, 2021, pp. 1226–1239
  * [60] Richard J Ladle et al.  “Conservation Culturomics”  In _Frontiers in Ecology and the Environment_ 14.5, 2016, pp. 269–275
  * [61] Richard J Ladle, Paul Jepson, Ricardo A Correia and Ana CM Malhado  “A culturomics approach to quantifying the salience of species on the global internet”  In _People Nat_ 1.4, 2019, pp. 524–532
  * [62] Mike Lewis et al.  “BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension”  arXiv, 2019  arXiv:1910.13461 [cs, stat]
  * [63] Joseph W. Millard, Richard D. Gregory, Kate E. Jones and Robin Freeman  “The Species Awareness Index as a Conservation Culturomics Metric for Public Biodiversity Awareness”  In _Conservation Biology_ 35.2, 2021, pp. 472–482
  * [64] S.k. Papworth et al.  “Quantifying the role of online news in linking conservation research to Facebook and Twitter”  In _Conservation Biology_ 29.3, 2015, pp. 825–833
  * [65] Silviu O. Petrovan et al.  “Post COVID-19: A Solution Scan of Options for Preventing Future Zoonotic Epidemics”  In _Biological Reviews_ 96.6, 2021, pp. 2694–2715
  * [66] Jean-Michel Roberge  “Using Data from Online Social Networks in Conservation Science: Which Species Engage People the Most on Twitter?”  In _Biodiversity and Conservation_ 23.3, 2014, pp. 715–726
  * [67] Uri Roll, Ricardo A Correia and Oded Berger-Tal  “Using machine learning to disentangle homonyms in large text corpora”  In _Conservation Biology_ 32.3 Wiley Online Library, 2018, pp. 716–724
  * [68] Chris Stokel-Walker  “Twitter May Have Lost More than a Million Users since Elon Musk Took Over”, 2022
  * [69] Andrew D Thaler, Naomi A Rose, A Mel Cosentino and Andrew J Wright  “Lions, whales, and the web: transforming moment inertia into conservation action”  In _Frontiers in Marine Science_ 4 Frontiers Media SA, 2017, pp. 292
  * [70] Reut Vardi, John C. Mittermeier and Uri Roll  “Combining Culturomic Sources to Uncover Trends in Popularity and Seasonal Interest in Plants”  In _Conservation Biology_ 35.2, 2021, pp. 460–471
  * [71] Diogo Veríssimo  “Trends in Digital Marketing for Biodiversity Conservation”, 2021
  * [72] Varsha Vijay, Christopher R. Field, Florian Gollnow and Kelly K. Jones  “Using Internet Search Data to Understand Information Seeking Behavior for Health and Conservation Topics during the COVID-19 Pandemic”  In _Biological Conservation_ 257, 2021, pp. 109078
  * [73] Joss Wright, Robert Lennox and Diogo Veríssimo  “Online Monitoring of Global Attitudes Towards Wildlife”  arXiv, 2020  arXiv:2007.11506 [cs]
  * [74] Peng Zhou et al.  “A Pneumonia Outbreak Associated with a New Coronavirus of Probable Bat Origin”  In _nature_ 579.7798 Nature Publishing Group, 2020, pp. 270–273

##  Appendix A Supplementary Information

###  A.1 Folk Taxonomy Graph Construction

To construct the graph, a node is created for every:

  * •

scientific name for a species (e.g. _Rhinolophus affinis_)

  * •

full common name for a species (e.g. “Intermediate horseshoe bat”)

  * •

substring shared between multiple common names (e.g. “Horseshoe bat” is part
of “Intermediate horseshoe bat” and “Greater horseshoe bat”) or substrings
(e.g. “Bat” is part of “Horseshoe bat” and “Fruit bat”)

Edges exist between the nodes representing the scientific name and the full
common names for each species, as well as between common names and their
substrings.

Table A1 shows the folk taxonomy entries that we designed using graph theory
applied to species’ common names.

Table A1: Table mapping folk taxonomy entities to scientific taxa. Order |  Scientific taxon | Folk taxon |  Positive keywords |  Negative keywords
---|---|---|---|---
Chiroptera |  Order Chiroptera | Bat |  bat |
Chiroptera |  Family Pteropodidae | Flying fox |  flying fox, pale xantharpy, acerodon, monkey-faced bat, greater nectar bat, fruit bat, north moluccan blossum-bat, rousette, golden bat of rodrigues, codot horsfield, woerman’s bat, blossom bat |  tube-nosed fruit bat
Chiroptera |  Family Hipposideridae, Family Rhinonlophidae | Horseshoe bat |  diadem leafnosed-bat, roundleaf bat, horseshoe-bat, horseshoe bat, great woolly horsehoe bat, trident bat, leaf-nosed bat, flower-faced bat |
Chiroptera |  Genus Glossophaga, Genus Craseonycteris, Genus Leptonycteris | Long tongued bat |  hog-nosed bat, long-nosed bat, bumblebee bat, long-tongued bat |
Chiroptera |  Genus Myotis, Genus Perimyotis | Myotis |  pond bat, bocage’s banana bat, van hasselts bat, social bat, bechstein’s bat, hodgson’s bat, lesser large-tooth bat, mouse-eared bat, ridley’s bat, water bat, ikonnikov’s bat, whiskered bat, welwitch’s bat, three-coloured bat, daubenton’s bat, indiana bat, fish-eating bat, grey bat, siliguri bat, geoffroy’s bat, large-footed bat, myotis, horsfield’s bat, rickett’s big-footed bat, little brown bat, brandt’s bat, siberian bat, morris’s bat, natterer’s bat, intermediate bat, hairy-faced bat, Descaleras bat |
Chiroptera |  Subfamily Vespertilioninae | Pipistrelle |  pipistrelle, pipistrelle bat, cape bat, anchieta’s bat, rohus bat, thai golden-throated bat, siam goldnecklet, dormer’s bat, ruppell’s bat, rusty bat, little indian bat, white-winged bat, sind bat, aloe bat, hottentot bat, indian pygmy bat, banana bat |  bocages banana bat
Chiroptera |  Family Emballonuridae, Family Phyllostomidae, Family Megadermatidae | Vampire bat |  ghost bat, vampire bat, false vampire, heart-nosed bat |
Proboscidea |  Family Elephantidae | Elephant |  elephant |
Pholidota |  Family Manidae | Pangolin |  pangolin |
Primates |  Genus Gorilla | Gorilla |  gorilla |

Table A2 displays the counts of news media articles for different taxa
classified as relevant verses irrelevant to biodiversity and categorized as
original or syndicated.

Table A2: The counts across taxa for original versus duplicate articles identified in our pipeline and relevant articles determined by zero-shot learning with a transformer model. | Predicted relevance: | True | False
---|---|---|---
Taxon | Original article |  |
Elephant | True | 86425 | 70548
False | 69335 | 54403
Gorilla | True | 21399 | 21206
False | 11715 | 9476
Pangolin | True | 6286 | 1495
False | 5976 | 1055
Bat | True | 77802 | 129058
False | 45452 | 68122
Flying fox | True | 1969 | 601
False | 1311 | 447
Myotis | True | 1103 | 103
False | 978 | 21
Horseshoe bat | True | 791 | 171
False | 745 | 257
Pipistrelle | True | 496 | 63
False | 489 | 45
Vampire bat | True | 341 | 195
False | 357 | 176
Long-tongued bat | True | 150 | 24
False | 79 | 9

Figure 8 shows the distribution of topics associated with relevant articles
for different taxa.

Figure 8: Chord diagrams depicting the co-occurrence of relevant topics for
the different taxa.

Figure 9 displays changes in volume for all of the focal taxa analyzed in the
pipeline.

Figure 9: Changes in volume through time. The solid vertical magenta line
denotes March 11, 2020, which was the date when the UN WHO declared COVID19 a
pandemic. The dashed vertical orange or blue lines correspond to any
significant breakpoints in the trend for GDELT or Twitter respectively, after
conducting Bonferroni family-wise error correction.

Figure 10 displays changes in mean article sentiment for all of the focal taxa
analyzed in the pipeline.

Figure 10: Changes in sentiment through time. The solid vertical magenta line
denotes March 11, 2020, which was the date when the UN WHO declared COVID19 a
pandemic. The dashed vertical orange or blue lines correspond to any
significant breakpoints in the trend for GDELT or Twitter respectively, after
conducting Bonferroni family-wise error correction.


# Upper Limits on Branching Ratios of Decays $\tau\to\ell\gamma\gamma$ and
$\tau\to\ell X$

Douglas A. Brymana,b, Shintaro Itoc, and Robert Shrockd (a) Department of
Physics and Astronomy, University of British Columbia, Vancouver British
Columbia, V6T 1Z1, Canada  (b) TRIUMF, 4004 Wesbrook Mall, Vancouver, British
Columbia V6T 2A3, Canada  (c) KEK, 1-1 Oho, Tsukuba, Ibaraki, 305-0801, Japan
(d) C. N. Yang Institute for Theoretical Physics and Department of Physics and
Astronomy,
Stony Brook University, Stony Brook, NY 11794, USA

###### Abstract

From analysis of data produced by the BABAR experiment, the first upper bounds
(90% C.L.) were obtained on the branching ratios $Br(\tau\to
e\gamma\gamma)<2.5\times 10^{-4}$ and $Br(\tau\to\mu\gamma\gamma)<5.8\times
10^{-4}$. In addition, improved upper bounds (95% C.L.) were found on
branching ratios $Br(\tau\to eX)<1.4\times 10^{-3}$ and $Br(\tau\to\mu
X)<2.0\times 10^{-3}$, where $X$ is an undetected weakly interacting boson
with mass $m_{X}<1.6$ GeV/$c^{2}$.

##  I Introduction

The violation of lepton family number has been firmly established by the
observation of neutrino oscillations, which also implies charged lepton family
(flavor) number violation (CLFV). Although no CLFV has been observed yet, it
is of fundamental interest, and searches for CLFV processes continue to be
pursued. In the Standard Model (SM) extended to include massive neutrinos
(generically denoted the $\nu$SM), the branching ratios for CLFV decays such
as $\mu\to e\gamma$, $\mu\to e\gamma\gamma$, $\mu\to ee\bar{e}$,
$\tau\to\ell\gamma$, and $\tau\to\ell\ell^{\prime}\bar{\ell}^{\prime}$, where
$\ell=e,\mu$ and $\ell^{\prime}=e,\mu$, are many orders of magnitude below the
level where they could be observed in existing or planned experiments. This
means that searches for these decays and similar CLFV processes are of great
interest as probes of physics beyond the $\nu$SM (BSM).

Other CLFV processes which are not present in the SM may involve new weakly
interacting bosons ($X$). For example, searches for $\mu\to eX$ were reported
in [1, 2, 3, 4, 5]. The emission of an $X$ boson has also been searched for in
$\pi^{+}$ decays [6, 7, 8] and $K^{+}$ decays [9, 10, 11, 12]. The ARGUS
experiment [13] at DESY reported limits for $Br(\tau\to\ell
X)/Br(\tau\to\ell\nu_{\tau}\bar{\nu}_{\ell})$.

Current upper bounds 111Unless otherwise indicated, all experimental upper
limits are given at the 90% confidence level (C.L.). on some CFLV $\tau$ decay
modes are listed in Table 1. In this paper, we used existing data to set the
first upper limits on the branching ratios of the CLFV decays $\tau\to
e\gamma\gamma$ and $\tau\to\mu\gamma\gamma$. We also examined the branching
ratios for the decays $\tau\to\ell X$.

Table 1: Upper limits on the branching ratios for some CLFV decays of the
$\tau$ lepton [14]. $Br$(decay) upper limit
---
$Br(\tau\to e\gamma)<3.3\times 10^{-8}$
$Br(\tau\to\mu\gamma)<4.4\times 10^{-8}$
$Br(\tau\to ee\bar{e})<2.7\times 10^{-8}$
$Br(\tau\to e\mu\bar{\mu})<1.7\times 10^{-8}$
$Br(\tau\to\mu e\bar{e})<1.5\times 10^{-8}$
$Br(\tau\to\mu\mu\bar{\mu})<2.1\times 10^{-8}$
$Br(\tau\to e\pi^{0})<0.80\times 10^{-7}$
$Br(\tau\to\mu\pi^{0})<1.1\times 10^{-7}$

##  II Theoretical Background

###  II.1 CLFV in the $\nu$SM

To accommodate the observed neutrino oscillations and associated violation of
lepton family number in the neutrino sector, the (renormalizable) SM
Lagrangian can be modified by adding a number $n_{s}$ of electroweak-singlet
neutrino fields $\nu_{i,R}$, $i=1,...,n_{s}$, conventionally written as right-
handed chiral fermions. With these, Yukawa terms are formed with the left-
handed lepton doublets which, via the vacuum expectation values of the Higgs
field, yield Dirac-type mass terms for neutrinos. The electroweak-singlet
neutrinos also generically lead to Majorana mass terms of the form
$\sum_{i,j}M^{(R)}_{ij}\nu_{i,R}^{T}C\nu_{j,R}+h.c.$ The diagonalization of
this combination of Dirac and Majorana mass terms yields the neutrino mass
eigenstates. The resultant unitary transformation relating the left-handed
chiral components of the mass eigenstates of the neutrinos, $\nu_{i,L}$, to
the weak eigenstates, $\nu_{a,L}$, is given by

$\nu_{a,L}=\sum_{i}U^{(\nu)}_{ai}\,\nu_{i,L}\ .$ (1)

The property that $U^{(\nu)}$ is different from the identity gives rise to
neutrino oscillations and the associated violation of lepton family number in
the neutrino sector. (There is, in general, also violation of total lepton
number in the $\nu$SM, due to the presence of Majorana mass terms.)

The diagonalization of the charged lepton mass matrix involves another unitary
matrix $U^{(\ell)}$, and the product of (the adjoint of) $U^{(\ell)}$ and
$U^{(\nu)}$ determines the form of the weak charged current:

$J_{\lambda}=\bar{\ell}_{L}\gamma_{\lambda}\nu_{\ell,L}=\sum_{a,i}\bar{\ell}_{a,L}\gamma_{\lambda}U_{ai}\nu_{i,L}\
,$ (2)

where $U$ is the lepton mixing matrix,

$U=U^{(\ell)\ \dagger}\,U^{(\nu)}\ .$ (3)

As an example of CLFV in the $\nu$SM, the branching ratio for
$\ell_{a}\to\ell_{b}+\gamma$ is [15, 16]

$Br(\ell_{a}\to\ell_{b}\gamma)=\frac{3\alpha_{em}}{32\pi}\bigg{|}\sum_{i}U_{ai}U_{bi}^{*}\,\frac{m_{\nu_{i}}^{2}}{m_{W}^{2}}\bigg{|}^{2}\
,$ (4)

where $\alpha_{em}=e^{2}/(4\pi)$ is the fine structure constant, and $a$ is
the family or generation index, with $\ell_{1}\equiv e$, $\ell_{2}\equiv\mu$,
and $\ell_{3}\equiv\tau$. Using current data on neutrino masses and lepton
mixing, the resultant SM predictions for the branching ratios for the decays
$\mu\to e\gamma$, $\tau\to e\gamma$, and $\tau\to\mu\gamma$ have values
$\mathrel{\raisebox{-2.58334pt}{$\stackrel{{\scriptstyle\textstyle<}}{{\sim}}$}}10^{-53}$,
far below a level that could be observed in any existing or planned
experiment. In passing, we recall the current upper limits on CLFV muon
decays, $Br(\mu\to e\gamma)<4.2\times 10^{-13}$ from the MEG experiment at PSI
[17], $Br(\mu\to e\gamma\gamma)<0.72\times 10^{-10}$ from the Crystal Box
experiment at LAMPF [18], and $Br(\mu\to ee\bar{e})<1.0\times 10^{-12}$ from
the SINDRUM experiment at SIN/PSI [19]. Since the decay
$\ell_{a}\to\ell_{b}\gamma\gamma$ involves emission of a second photon, as
compared with $\ell_{a}\to\ell_{b}\gamma$, it follows that for the $\nu$SM, up
to logarithmic terms,

$Br(\ell_{a}\to\ell_{b}\gamma\gamma)\sim\alpha_{em}\,Br(\ell_{a}\to\ell_{b}\gamma).$
(5)

(Similarly,
$Br(\ell_{a}\to\ell_{b}\ell_{c}\bar{\ell}_{c})\sim\alpha_{em}Br(\ell_{a}\to\ell_{b}\gamma)$
in the $\nu$SM.)

###  II.2 Possible Physics Beyond the Standard Model Contributing to
$\tau\to\ell\gamma$ and $\tau\to\ell\gamma\gamma$

Although CLFV processes are predicted to be unobservably small in the $\nu$SM,
there are many models of physics beyond the $\nu$SM that generically predict
CLFV at observable rates. While none of these models has been confirmed by
experiment, they remain of interest since they address incomplete aspects of
the SM. One such aspect concerns the Higgs mass. There is a fine-tuning
problem associated with this quantity since one-loop corrections to the Higgs
mass squared are quadratically sensitive to the highest mass scale in an
ultraviolet completion of the SM, such as a grand unified theory. Two early
ideas for BSM physics that addressed this problem were supersymmetry (SUSY)
and dynamical electroweak symmetry breaking (EWSB), and both of these
generically predicted CLFV (as well as a number of flavor-changing neutral-
current processes) at observable levels. For example, supersymmetric
extensions of the SM predicted the decay $\mu\to e\gamma$ to occur at
observable levels [20, 21, 22, 23], and this is also true of
$\tau\to\ell\gamma$. Early SUSY models with light neutralinos $\tilde{\chi}$
allowed the decay $\mu\to e\tilde{\chi}\tilde{\chi}$, which would be distinct
from SM $\mu$ decay [24]. Substantial contributions to $\mu\to e\gamma\gamma$
and $\tau\to\ell\gamma\gamma$ would also be expected in such SUSY theories.
Although searches for supersymmetric particles at the Fermilab Tevatron and at
the CERN Large Hadron Collider (LHC) have yielded null results so far, there
still remains the possibility of supersymmetry characterized by a SUSY-
breaking scale that is larger than the electroweak scale.

Dynamical EWSB models also predict CLFV processes at possibly observable
levels [25, 26, 27]. A relevant property of reasonably ultraviolet-complete
dynamical EWSB models is the generic presence of three sequential stages of
breaking of an asymptotically free chiral gauge symmetry in the ultraviolet.
These symmetry-breaking scales are $\Lambda_{1}\sim O(10^{3})$ TeV,
$\Lambda_{2}\sim O(10^{2})$ TeV, and $\Lambda_{3}\sim O(10)$ TeV. The low-
energy effective field theory resulting from this sequential breaking involves
a vectorial gauge interaction that becomes strongly coupled at the TeV scale
and produces the electroweak symmetry breaking [26]. Communication of the
dynamical EWSB to the SM fermions of the first generation occurs via exchanges
of virtual vector bosons with masses $\sim\Lambda_{1}$; since these masses
occur in the denominators of the vector boson propagators, this naturally
explains why quarks and leptons of the first generation have the smallest
masses. Correspondingly, the property that $\Lambda_{3}$ is the lowest of
these breaking scales is associated with the fact that the quarks and leptons
of the third generation have the largest SM fermion masses. The feature that
the third generation is associated with the lowest scale, $\Lambda_{3}$, could
give rise to enhanced CLFV processes involving the $\tau$ lepton [28]. Modern
versions of dynamical EWSB models typically involve quasi-conformal behavior,
which can result naturally from an approximate infrared fixed point of the
renormalization group equations describing the strongly coupled vectorial
gauge interaction [29, 30, 31]. In general, in these models, the observed
Higgs is a composite state. These dynamical EWSB models are tightly
constrained by precision electroweak data, the observed agreement of the Higgs
boson with SM predictions, and, more generally, the non-observation of any BSM
Higgs properties at the LHC.

A large variety of other BSM theories predict CLFV effects at potentially
observable levels. These could have the potential to alter the $\nu$SM
relation (5). For example, in theories with doubly charged leptons, the ratio
of branching ratios $Br(\tau\to\ell\gamma\gamma)/Br(\tau\to\ell\gamma)$ can be
substantially enhanced relative to the $O(\alpha_{em})$ relation in Eq. (5),
just as was true of the ratios $Br(\mu\to ee\bar{e})/Br(\mu\to e\gamma)$ and
$Br(\mu\to e\gamma\gamma)/Br(\mu\to e\gamma)$ [32]; some recent studies of
theories with doubly charged leptons [33] provide experimental constraints.

Of particular interest for $\tau\to\ell X$ decays are models with a light
pseudo-Nambu-Goldstone boson (NGB) or a massless NGB that can couple to
fermions in a flavor-violating manner [34, 35]. These arise in models that
hypothesize a “horizontal” symmetry mixing SM fermions transforming in the
same manner under the SM gauge group, $G_{\rm SM}={\rm SU}(3)_{c}\otimes{\rm
SU}(2)_{L}\otimes{\rm U}(1)_{Y}$, namely the sets $(e,\mu,\tau)_{L}$,
$(e,\mu,\tau)_{R}$, and so forth for the neutrinos and quarks. With the
hypothesized generational (i.e., family) symmetry taken to be global, a
consequence would be that the spontaneous breaking of the flavor symmetry
would lead to massless, spinless NGB(s) (often called familons). In the
presence of some explicit breaking of the generational symmetry, the
spontaneous breaking yields light NGB(s), with mass(es) determined by the
relative sizes of explicit and spontaneous symmetry breaking. These NGBs are
often called “axion-like particles” (ALPS). CLFV effects may also be
associated with spontaneous breaking of total lepton number and resultant
majorons [37, 36, 38, 39]. Some more recent studies and reviews include [40,
41, 42, 43, 44, 45, 46, 47, 48, 49].

Models featuring extra $Z^{\prime}$ vector bosons with flavor-non-diagonal
couplings can yield CLFV effects at observable levels (e.g., [50, 51]). These
could contribute to CLFV decays such as $\tau\to\ell\gamma\gamma$ and
$\tau\to\ell\gamma$. CLFV processes have also been studied in models with
extra (spatial) dimensions and fermion fields having localized wave functions
in these extra dimensions [52, 53]. An appeal of these models is that they can
produce a strong hierarchy in SM fermion masses via moderate separation of
fermion wave function centers in the extra dimensions [54, 55]. Connections
between reported anomalies in $B$ meson decays, $e-\mu$ universality
violation, and models with CFLV have been discussed in a number of studies and
are reviewed, e.g., in [56]. Although dark matter is, in principle,
independent of CFLV, there may be connections between these in certain models
[57].

##  III $\tau\to\ell\gamma\gamma$

In the following we discuss the angular distribution expected for the decay
products $\tau\to\ell\gamma\gamma$. Then, we use existing data on searches for
$\tau\to e\gamma$ and $\tau\to\mu\gamma$ from the BABAR experiment at SLAC
[58] to derive the first upper limits on the branching ratios for these
decays, $\tau\to e\gamma\gamma$ and $\tau\to\mu\gamma\gamma$. (See also the
recent result from Belle [59], which improves slightly on the upper limit on
$B(\tau\to\mu\gamma)$ in [58].) In abstract notation, these decays are of the
form $\ell_{a}\to\ell_{b}\gamma\gamma$ with the generational indices $a=3$ and
$b=1,\ 2$, respectively.

###  III.1  Angular Distribution for $\tau\to\ell\gamma\gamma$

A calculation of the decay rate for $\mu\to e\gamma\gamma$ was originally
carried out in 1962 by Dreitlein and Primakoff [61] and was applied to the
data on searches for $\mu\to e\gamma$ to set the upper bound $Br(\mu\to
e\gamma\gamma)<5\times 10^{-6}$. This procedure including a general expression
for the angular distribution of the photons (discussed below) was applied by
Bowman _et al._ [62] to data from two contemporaneous experiments searching
for $\mu\to e\gamma$ [63, 64] to derive the upper limit $Br(\mu\to
e\gamma\gamma)<5\times 10^{-8}$ [65, 66, 67, 68, 69].

For our analysis, we do not assume a particular BSM theory, but instead use an
effective field theory method. In general, for a decay of the form
$\ell_{a}\to\ell_{b}\gamma\gamma$, the operators that contribute to leading
order to the effective Lagrangian ${\cal
L}_{eff,\ell_{a}\ell_{b}\gamma\gamma}$ are lepton bilinears contracted with
$F_{\alpha\beta}F^{\alpha\beta}$ or $F_{\alpha\beta}\tilde{F}^{\alpha\beta}$
(with coefficients given in Eq. (9) below), where $F^{\alpha\beta}$ is the
electromagnetic field strength tensor and
$\tilde{F}^{\alpha\beta}=(1/2)\epsilon^{\alpha\beta\lambda\rho}F_{\lambda\rho}$
is its dual. At more suppressed levels, there are additional operators
involving derivatives. In $d$ spacetime dimensions, the mass dimension of an
operator ${\cal O}$ comprised of a lepton bilinear, a product of $FF$ or
$F\tilde{F}$, and $n_{{}_{\partial}}$ derivatives, is ${\rm dim}({\cal
O})=2d-1+n_{{}_{\partial}}$. It follows that the coefficient $c_{\cal O}$ has
mass dimension ${\rm dim}(c_{\cal O})=-(d-1+n_{{}_{\partial}})$, i.e., for the
physical case $d=4$, ${\rm dim}(c_{\cal O})=-(3+n_{{}_{\partial}})$. One can
thus write

$c_{\cal O}=\frac{\bar{c}_{\cal O}}{(\Lambda_{\cal O})^{3+n_{{}_{\partial}}}}\
,$ (6)

where $\bar{c}_{\cal O}$ is dimensionless and $\Lambda_{\cal O}$ denotes a
scale of BSM physics responsible for the appearance of the operator ${\cal
O}$. Since in both of the decays $\tau\to\ell\gamma\gamma$ with $\ell=e$ or
$\ell=\mu$, $m_{\ell}\ll m_{\tau}$, the only mass that enters into the phase
space kinematics of the decay is $m_{\tau}$. Because the derivatives yield
factors of momenta in the amplitude, and the sizes of these momenta are set
(in the $\tau$ rest frame) by $m_{\tau}$, it follows that the contribution of
an operator with $n_{{}_{\partial}}$ derivatives is suppressed by the factor
$(m_{\tau}/\Lambda_{\cal O})^{n_{{}_{\partial}}}$. The agreement of the
$\nu$SM with current data implies that the scales $\Lambda_{\cal O}$ are much
larger than $m_{W,Z}$, and hence $(m_{\tau}/\Lambda_{\cal
O})^{n_{{}_{\partial}}}\ll 1$. Therefore, operators with derivatives are
expected to make a negligible contribution to the amplitude for
$\tau\to\ell\gamma\gamma$. The effective Lagrangian for
$\tau\to\ell\gamma\gamma$ can then be written, retaining non-negligible terms,
as

$\displaystyle{\cal L}_{eff,\tau\ell\gamma\gamma}$ $\displaystyle=$
$\displaystyle
c_{\tau\ell\gamma\gamma,LR;FF}[\bar{\ell}_{L}\tau_{R}]F_{\alpha\beta}F^{\alpha\beta}+c_{\tau\ell\gamma\gamma,RL;FF}[\bar{\ell}_{R}\tau_{L}]F_{\alpha\beta}F^{\alpha\beta}+c_{\tau\ell\gamma\gamma,LR;F\tilde{F}}[\bar{\ell}_{L}\tau_{R}]F_{\alpha\beta}\tilde{F}^{\alpha\beta}$
(7) $\displaystyle+$ $\displaystyle
c_{\tau\ell\gamma\gamma,RL;F\tilde{F}}[\bar{\ell}_{R}\tau_{L}]F_{\alpha\beta}\tilde{F}^{\alpha\beta}+h.c.\
,$ (9)

where the subscript $LR$ on $c_{\tau\ell\gamma\gamma,LR;FF}$ refers to the
chirality structure in the associated lepton bilinear,
$[\bar{\ell}_{L}\tau_{R}]$, and similarly for the other coefficients. Without
loss of generality, we will introduce a single effective mass scale
$\Lambda_{\tau\ell\gamma\gamma}$ to characterize the CFLV physics responsible
for the decay $\tau\to\ell\gamma\gamma$; any differences in the actual mass
scales characterizing different operators ${\cal O}$ are absorbed into the
values of the dimensionless coefficients $\bar{c}_{\cal O}$. Then Eq. (6)
reads

$c_{\cal O}=\frac{\bar{c}_{\cal O}}{\Lambda_{\tau\ell\gamma\gamma}^{3}}$ (10)

for each of the coefficients $c_{\tau\ell\gamma\gamma,LR;FF}$,
$c_{\tau\ell\gamma\gamma,RL;FF}$, $c_{\tau\ell\gamma\gamma,LR;F\tilde{F}}$,
and $c_{\tau\ell\gamma\gamma,RL;F\tilde{F}}$ in ${\cal
L}_{eff,\tau\ell\gamma\gamma}$. Let us denote the four-momenta of the $\tau$,
the final-state charged lepton $\ell$, and the two photons as $p_{\tau}$,
$p_{\ell}$, $k_{1}$, and $k_{2}$, respectively, and Lorentz-scalar products of
two four-vectors as $p_{\tau}\cdot p_{\ell}$, etc. Let us further denote the
matrix element for this decay as ${\cal M}_{\tau\to\ell\gamma\gamma}$. As
usual, the amplitude is Bose-symmetrized with respect to the interchange of
the identical bosons (photons) in the final state. With the above input ${\cal
L}_{eff,\tau\ell\gamma\gamma}$, the square of the amplitude has a kinematic
factor $(p_{\tau}\cdot p_{\ell})(k_{1}\cdot
k_{2})^{2}=m_{\tau}E_{\ell}[E_{\gamma_{1}}E_{\gamma_{2}}(1-\cos\theta_{\gamma_{1}\gamma_{2}})]^{2}$,
and the differential decay rate is

$\displaystyle\frac{d\Gamma_{\tau\to\ell\gamma\gamma}}{dE_{\gamma_{1}}dE_{\gamma_{2}}d\cos\theta_{\gamma\gamma}}\propto\bigg{(}\frac{\sum_{\cal
O}|\bar{c}_{\cal
O}|^{2}}{\Lambda_{\tau\ell\gamma\gamma}^{6}}\bigg{)}\,E_{\ell}(E_{\gamma_{1}}E_{\gamma_{2}})^{2}(1-\cos\theta_{\gamma\gamma})^{2}\
,$ (11)

where

$\sum_{\cal O}|\bar{c}_{\cal
O}|^{2}=|\bar{c}_{\tau\ell\gamma\gamma,LR;FF}|^{2}+|\bar{c}_{\tau\ell\gamma\gamma,RL;FF}|^{2}+|\bar{c}_{\tau\ell\gamma\gamma,LR;F\tilde{F}}|^{2}+|\bar{c}_{\tau\ell\gamma\gamma,RL;F\tilde{F}}|^{2}\
,$ (12)

$E_{\ell}$, $E_{\gamma_{1}}$, and $E_{\gamma_{2}}$ are the energies of the
daughter lepton $\ell$ and the two photons, respectively, and
$\theta_{\gamma\gamma}$ is the angle between the 3-momenta of the photons
(i.e.,
$\cos\theta_{\gamma\gamma}=({\vec{k}}_{1}\cdot{\vec{k}}_{2})/(E_{\gamma_{1}}E_{\gamma_{2}})$
) in the $\tau$ rest frame.

We note that a two-photon final state could also arise as a radiative
correction to the decay $\tau\to\ell\gamma$, via emission of the second photon
from the initial $\tau$ or from the final-state $\ell$, where $\ell=e$ or
$\mu$. An event of this type would have an angular distribution different from
that of an event in which the two photons originated directly as a consequence
of the BSM physics, and the associated ${\cal L}_{eff,\tau\ell\gamma\gamma}$
in Eq. (9). Events in which a second photon is emitted as a radiative
correction to a $\tau\to\ell\gamma$ decay were considered by the BABAR
experiment [58], were modelled by the event simulation programs used in that
experiment, and were taken into account in their upper limits on
$Br(\tau\to\ell\gamma)$.

###  III.2 Study of $\tau\to\ell\gamma\gamma$ based on BABAR limits on
$\tau\to\ell\gamma$

.

The BABAR experiment searches for $\tau\to\ell\gamma$ decays [58] were
performed at the SLAC PEP-II $e^{+}e^{-}$ storage rings, primarily using
center-of-mass (c.m.) energy $\sqrt{s}\simeq 10.6$ GeV at the $\Upsilon(4S)$
resonance. The BABAR detector is described in Ref. [60]. Charged particles
were reconstructed as tracks with a silicon vertex tracker and a drift chamber
inside a 1.5 T solenoidal magnet. A CsI(Tl) electromagnetic calorimeter
identified electrons and photons, and a ring imaging Cherenkov detector
identified charged pions and kaons. The flux return of the solenoid was
instrumented with resistive plate chambers, and limited streamer tubes were
used to identify muons.

Events ascribed to the reaction $e^{+}e^{-}\to\tau^{+}\tau^{-}$ were selected,
and events of the form $\tau^{\pm}\to\ell^{\pm}\gamma$, were identified by a
$\ell,\gamma$ pair with an invariant mass and total energy in the c.m. frame
close to $m_{\tau}=1.777$ GeV/$c^{2}$ and $\sqrt{s}/2$, respectively. Another
$\tau^{\pm}$ decay in the opposite detector hemisphere was used as a tag.
Important backgrounds arose from the reaction
$e^{+}e^{-}\to\tau^{+}\tau^{-}\gamma$ yielding a hard photon when one $\tau$
underwent a SM decay to an $\ell$ and a neutrino anti-neutrino pair. Other
backgrounds for the $\tau\to\ell\gamma$ search arose from the reaction
$e^{+}e^{-}\to\ell^{+}\ell^{-}\gamma$ and from hadronic $\tau$ decays with
particle mis-identification.

The signal-side hemisphere was required to contain one photon with c.m. energy
$>1$ GeV, with no other photon with energy $>100$ MeV in the laboratory frame.
The signal had to contain one track identified as an electron or muon within
the calorimeter acceptance with c.m. momentum less than $0.77\sqrt{s}/2$.
Muons were also required to have momentum greater than 0.7 GeV/$c$ in the
laboratory frame. In addition, the cosine of the opening angle between the
signal track and signal photon was required to be less than 0.786
characterizing the back-to-back distribution of $\tau\to\ell\gamma$ events in
the $\tau$ rest frame. Neural net cuts were also applied to the BABAR data.

Signal decays were identified by two kinematic variables: the energy
difference $\Delta E=E_{\ell\gamma}^{c.m.}-\sqrt{s}/2$, where
$E_{\ell\gamma}^{c.m.}$ is the c.m. energy of $\ell\gamma$ pair, and the beam
energy constrained $\tau$ mass (mEC), obtained from a kinematic fit after
requiring the c.m. $\tau$ energy to be $\sqrt{s}/2$; the origin of the
$\gamma$ candidate was assigned to the point of closest approach of the signal
lepton track to the $e^{+}e^{-}$ collision axis [58].

Limits on the decays $\tau\to e\gamma\gamma$ and $\tau\to\mu\gamma\gamma$ were
obtained using the results of the BABAR experiment searching for $\tau\to
e\gamma$ and $\tau\to\mu\gamma$ decays. Using Eq. (11), we simulated
$1{\times}10^{7}$ events for each $\tau\to\ell\gamma\gamma$ process applying
momentum and energy resolutions (smearing) for the charged track and photons
as reported by Ref. [60] and applying the cuts indicated above (except for the
neural net cuts) to select events. Then, without the resolution effects
applied, we constructed the mEC and $\Delta E$ variables for the
$\tau\to\ell\gamma\gamma$ events which passed the cuts and were within the
BABAR detector acceptance. The mEC and $\Delta E$ variables were then smeared
according to their reported resolutions [58].

Figure 1 shows a plot of mEC vs. $\Delta E$ for simulated $\tau\to
e\gamma\gamma$ events after the cuts and resolution smearing. Compared with
$\tau\to e\gamma$ in Ref. [58], the plot of mEC vs. $\Delta E$ for $\tau\to
e\gamma\gamma$ is widely distributed due to the requirement for the second
gamma to have $E<100$ MeV if in the signal side hemisphere or to be outside
the detector acceptance. The red ellipse in Figure 1 represents the signal
region for ${\tau}{\to}e{\gamma}$ used by the BABAR analysis including the
observed shifts in position due to radiative effects [58]. This elliptical
region contains the simulated $\tau\to e\gamma\gamma$ events which would have
been classified as consistent with the $\tau\to e\gamma$ signal representing
an efficiency of $\epsilon_{e\gamma\gamma}=1.2\times 10^{-4}$ compared to
$\epsilon_{e\gamma}=0.50$ for our simulation efficiency for $\tau\to e\gamma$.
The estimated uncertainty in the ratio
$\epsilon_{e\gamma}/\epsilon_{e\gamma\gamma}$ (used below) is approximately
10%.

Figure 1: mEC vs. $\Delta E$ for simulated $\tau\to e\gamma\gamma$ events. The
red ellipse indicates the signal region where events would have passed cuts
for $\tau\to e\gamma$ [58].

To obtain the limits on $\tau\to\ell\gamma$, BABAR used the numbers of
observed events and the numbers of the expected background events in the
signal ellipse leading to $Br(\tau\to e\gamma)<3.3\times 10^{-8}$ and
$Br(\tau\to\mu\gamma)<4.4\times 10^{-8}$. For $\tau\to e\gamma$
($\tau\to\mu\gamma$), 0 (2) events were observed and the expected background
was $1.6\pm 0.4$ ($3.6\pm 0.7$), respectively. In order to avoid complications
of estimating the expected backgrounds for $\tau\to\ell\gamma$ in the presence
of $\tau\to\ell\gamma\gamma$ decays [70], we used a conservative approach and
based the following limits on only the number of events observed by BABAR in
the signal ellipses: $Br^{\prime}(\tau\to e\gamma)<6.1\times 10^{-8}$ and
$Br^{\prime}(\tau\to\mu\gamma)<9.1\times 10^{-8}$.

Then, we found the limit

$Br(\tau\to e\gamma\gamma)<\frac{Br^{\prime}(\tau\to
e\gamma)\times\epsilon_{e\gamma}}{\epsilon_{e\gamma\gamma}}=2.5\times
10^{-4}.$ (13)

For the $\tau\to\mu\gamma\gamma$ case, we had
$\epsilon_{\mu\gamma\gamma}=7.2\times 10^{-5}$ and $\epsilon_{\mu\gamma}=0.46$
resulting in

$Br(\tau\to\mu\gamma\gamma)<\frac{Br^{\prime}(\tau\to\mu\gamma)\times\epsilon_{\mu\gamma}}{\epsilon_{\mu\gamma\gamma}}=5.8\times
10^{-4}.$ (14)

The estimated uncertainty in the ratio
$\epsilon_{\mu\gamma}/\epsilon_{\mu\gamma\gamma}$ is approximately 10%.

##  IV $\tau\to\ell X$

In this section we obtained new constraints on the decays $\tau\to\ell X$
where $X$ is a weakly interacting neutral boson that escapes without being
detected. The latter condition is satisfied if the lifetime $\tau_{X}$ is
sufficiently long or if $X$ decays invisibly. Theoretical motivations for
searching for such emission were discussed in Section II.

The signature for the decay $\tau\to\ell X$ is a monochromatic peak in the
energy of the daughter lepton $\ell$ in the $\tau$ rest frame at the value

$E_{\ell}=\frac{m_{\tau}^{2}+m_{\ell}^{2}-m_{X}^{2}}{2m_{\tau}}$ (15)

where $m_{X}$ is the mass of the $X$ particle. This type of search involves an
analysis of the energy or momentum spectrum of the daughter lepton in the
$\tau$ decay. A different approach to setting an upper limit on $Br(\tau\to
eX)$ and $Br(\tau\to\mu X)$ is based on the fact that if such events occurred
and were included together with events from the corresponding SM leptonic
decays of the $\tau$, they would alter the observed rates of the respective
decays.

Measurements of the individual branching ratios for
$\tau\to\nu_{\tau}e\bar{\nu}_{e}$ and $\tau\to\nu_{\tau}\mu\bar{\nu}_{\mu}$
have been carried out, with the results [14]

$Br(\tau\to\nu_{\tau}e\bar{\nu}_{e})=0.1782\pm 0.0004$ (16)

and

$Br(\tau\to\nu_{\tau}\mu\bar{\nu}_{\mu})=0.1739\pm 0.0004\ .$ (17)

The measured branching ratios (16) and (17) and the $\tau$ lifetime
$\tau_{\tau}=(2.903\pm 0.005)\times 10^{-13}$ s [14] can be used to obtain the
decay rates to compare with SM calculations. Using the formulation in [71],
the calculated values for the branching ratios (denoted by superscript $(c)$)
are $Br^{(c)}(\tau\to\nu_{\tau}e\bar{\nu}_{e})=0.17781\pm 0.00031$ and
$Br^{(c)}(\tau\to\nu_{\tau}\mu\bar{\nu}_{\mu})=0.17293\pm 0.00030$. Then, the
ratios of experimental to calculated decay rates are [72, 73]

$S_{\tau\to e}=\Gamma_{\tau\to e}/\Gamma^{(c)}_{\tau\to e,\leavevmode\nobreak\
{\rm SM}}=1.0022\pm 0.0028$ (18)

and

$S_{\tau\to\mu}=\Gamma_{\tau\to\mu}/\Gamma^{(c)}_{\tau\to\mu,\leavevmode\nobreak\
{\rm SM}}=1.0056\pm 0.0029$ (19)

with the following 95% C.L. [74] limits

$S_{\tau\to e}<1.008$ (20)

and

$S_{\tau\to\mu}<1.011\ .$ (21)

Eqs. (20) and (21) correspond to the 95% C.L. limits on the branching ratios
of $\tau\to\ell X$ relative to $\tau\to\ell\nu\bar{\nu}$

$\frac{Br(\tau\to eX)}{Br(\tau\to\nu_{\tau}e\bar{\nu}_{e})}<0.008$ (22)

and

$\frac{Br(\tau\to\mu X)}{Br(\tau\to\nu_{\tau}\mu\bar{\nu}_{\mu})}<0.011\ .$
(23)

These limits are plotted in Fig. 2

Figure 2: Limits (95% C.L.) on $Br(\tau\to\ell
X)/Br(\tau\to\ell\nu_{\tau}\bar{\nu}_{\ell})$ from ARGUS [13] (filled points)
and this work (solid lines).

along with the previous results from the ARGUS experiment [13]. Using the
measured $\tau\to\nu_{\tau}\ell\bar{\nu}_{\ell}$ branching ratios in Eqs. (16)
and (17), we found $Br(\tau\to eX)<1.4\times 10^{-3}$ and $Br(\tau\to\mu
X)<2.0\times 10^{-3}$. The limits found for $\tau\to\ell X$ decays also apply
to three-body decays of the form $\tau\to\ell XX$, for which no previous
bounds have been reported.

##  V Conclusions

Using an analysis of data from searches for $\tau\to e\gamma$ and
$\tau\to\mu\gamma$ performed by the BABAR experiment, we have obtained the
first upper limits on the branching ratios $Br(\tau\to e\gamma\gamma)$ and
$Br(\tau\to\mu\gamma\gamma)$. We have also presented improved upper limits on
$Br(\tau\to\ell X)$ where $\ell$ denotes $e$ or $\mu$ and $X$ is a weakly
interacting boson with mass $m_{X}<1.6$ GeV/$c^{2}$ that escapes detection. We
expect that these decay modes can be searched for with considerably higher
sensitivity at Belle II [75].

###### Acknowledgements.

We would like to thank C. Hearty for reading the manuscript and B. Velghe for
technical assistance. This work was supported by NSERC grant no.
SAPPJ-2018-0017 (Canada); JSPS KAKENKI grant No. 19K03888 (Japan); and the NSF
Grant NSF-PHY-1915093 (U.S.).

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