Characterizing Engagement Dynamics across Topics on Facebook

Gabriele Etta1, Emanuele Sangiorgio2, Niccolò Di Marco3, Michele Avalle1,
Antonio Scala4, Matteo Cinelli1, Walter Quattrociocchi1

1 Center of Data Science and Complexity for Society, Department of Computer
Science, Sapienza Università di Roma

2 Department of Social and Economic Sciences, Sapienza Università di Roma

3 Department of Mathematics and Computer Science, University of Florence,
Italy

4 ISC-CNR UoS Sapienza, Rome,Italy

*<EMAIL_ADDRESS>
## Abstract

Social media platforms heavily changed how users consume and digest
information and, thus, how the popularity of topics evolves. In this paper, we
explore the interplay between the virality of controversial topics and how
they may trigger heated discussions and eventually increase users’
polarization. We perform a quantitative analysis on Facebook by collecting
$\sim 57M$ posts from $\sim 2M$ pages and groups between 2018 and 2022,
focusing on engaging topics involving scandals, tragedies, and social and
political issues. Using logistic functions, we quantitatively assess the
evolution of these topics finding similar patterns in their engagement
dynamics. Finally, we show that initial burstiness may predict the rise of
users’ future adverse reactions regardless of the discussed topic.

## Introduction

The advent of social media platforms changed how users consume information
online [1, 2, 3, 4]. The micro-blogging features on Twitter and Facebook,
combined with a direct interaction between news producers and consumers, have
remarkably affected how people get informed, shape their own opinions, and
debate with other peers online [5, 6, 7]. Over the years, following the
business model of social media platforms, news outlets and producers attempted
to maximize the time spent by users on their contents [8, 9], giving birth to
the concept of attention economy [10]. The term refers to the users’ limited
capability and time to process all information they interact with [11, 12,
13]. The transition toward a news ecosystem shaped on social media platforms
unveiled patterns in information consumption at multiple scales [14, 15],
which contributed to the emergence of the polarisation phenomenon, and the
formation of like-minded groups called echo chambers [16, 17, 18]. Within echo
chambers, characterized by homophily in the interaction network and bias in
information diffusion towards like-minded peers, selective exposure [19] is a
significant driver for news consumption [17]. The combination of echo chambers
and selective exposure makes users more likely to ignore dissenting
information [20], choosing to interact with narratives adhering to their point
of view [15, 21].

Several studies explored the existence of these mechanisms in many topics
concerning political elections, public health, climate change, and
trustworthiness of the news sources [15, 21, 22, 23, 24, 25, 26, 27, 28, 29].
Findings indicate neither the topic nor the quality of information explains
the users’ opinion-formation process. Instead, several studies observed how
the virality of discussions can increase the likelihood of inducing
polarization, hate speech, and toxic behaviors [30, 31, 32], highlighting how
recommendation algorithms may have a role in shaping the news diet of users.

Therefore, it is necessary to provide a better understanding of how user
interest evolves in online debates. To achieve this goal, we provide a
quantitative assessment of the dynamics underlying user interest in news
articles about different topics. In this paper, we analyze the engagement
patterns produced by $\sim 57M$ posts on Facebook related to $\sim 300$
topics, involving a total of $\sim 2M$ posting pages and groups over a period
that ranges from $2018$ to $2022$. We first provide a quantitative assessment
of topics’ resonance through time, extracting insightful parameters from their
engagement evolution. Then, we exploit the obtained parameters by assessing
relationships with the sentiment expressed by users through their positive and
negative reactions. Our results show that topics are generally characterized
by an interest that constantly increases since the appearance of the first
post. We find that topics’ interactions grow with permanent intensity, even
for prolonged periods, indicating how interest is a cumulative process that
takes time. We statistically validate this result by comparing parameters
across topic categories, discovering no differences in the evolution of the
engagement. Indeed, regardless of their category, topics keep users engaged
steadily over time, and their lifetime progression seems thus unrelated to its
thematic field. Finally, we find that topics with sudden virality tend to
trigger more controversial and heterogeneous interactions. In turn, topics
with a steady evolution exhibit more positive and homogeneous reaction types.
This difference in the sentiment of reactions, and the protracted duration of
topics’ lifetime, are both upshots consistent with the emergence of selective
exposure as a driver of news consumption.

## Materials and Methods

This section describes the data collection process, the topic extraction
process, the models and the metrics employed in assessing collective
attention.

### Overview of the data collection process

The data collection process comprises several parts, as described in Fig. 1.
We start by creating a sample of news articles from the GDELT event database
[33], and then we process the articles’ text to obtain a set of representing
terms. Consequently, we apply the Louvain community detection algorithm [34]
on the co-occurrence term network to identify the topics of interest. The
terms representing these topics will serve as input for the collection of
posts from Facebook.

Figure 1: Summary of the analysis workflow followed in the current study. News
articles are collected from the GDELT Database, and their corpus is extracted,
cleaned and analyzed to retrieve the most representing terms. The co-
occurrence network built upon these terms serves as an input for the Louvain
community detection algorithm to identify keyword clusters. Independent
labellers then analyze these clusters to identify the subset of words that
represent the topic under consideration, which are then used on Crowdtangle to
retrieve the Facebook posts relating to those events.

#### News Extraction from GDELT

The GDELT (Global Database of Events, Language, and Tone) Project [35],
powered by Google Jigsaw, is a database of global human society which monitors
the world’s broadcast, print, and web news from nearly every corner of every
country in more than 100 languages. It identifies the people, locations,
organisations, themes, sources, emotions, counts, quotes, images and events
driving our global society every second of every day [36]. We gathered news
articles from the GDELT 2.0 Event Database [33], which can store new world’s
breaking events every 15 minutes and translates the corresponding news
articles in 65 languages, representing 98.4% of its daily non-English
monitoring volume [37]. The analysis covers a period between $1/1/2018$ and
$13/5/2022$, collecting $50$ news articles each week for a total of $\sim
79K$.

#### Extracting representative keywords from news articles

To clean and extract the most representative keywords of each news article, we
employed the newspaper3k Python package [38]. We initially extracted words
from the body of the article, excluding stopwords and numbers. Then, we
computed the word frequency $f(w,i)$ for each word $w$ in article $i$.
Finally, we sorted words in descending order according to their frequency,
keeping the top 10 most frequent words.

#### Topic Extraction from News Article’s Keywords

The list of terms with the corresponding news articles can be formalised as a
bipartite graph $G=(T,A,E)$ whose partitions $T$ and $A$ represent the set of
terms $t\in T$ and the articles $a\in A$ respectively, for which an edge
$(t,a)\in E$ exists if a term $t$ is present in an article $a$. By projecting
graph G on its terms $T$ we obtain an undirected graph $P$ made up of nodes
$t\in T$, which are connected if they share at least one news article.
We perform community detection on the nodes of $P$ by employing the Louvain
algorithm [34]. As a result, we obtain a set of clusters $C$, where each
cluster $c\in C$ contains a list of keywords that are assumed to be
semantically related to a topic. We then asked a pool of three human labellers
to select, for each community, from two to three terms they considered the
most representative to identify a topic unambiguously.

#### Data collection of Facebook posts

The news articles obtained from the GDELT Event Database do not contain
information helpful in estimating the attention they generate online. To
include the dimension of user engagement, we employ each topic’s set of
representative terms to collect Facebook data over a period that goes from
$01/01/2018$ to $05/05/2022$. The data was obtained using CrowdTangle [39], a
Facebook-owned tool that tracks interactions on public content from Facebook
pages, groups, and verified profiles. CrowdTangle does not include paid ads
unless those ads began as organic, non-paid posts that were subsequently
“boosted” using Facebook’s advertising tools. CrowdTangle also does not store
data regarding the activity of private accounts or posts made visible only to
specific groups of followers.

The collection process produced a total of $\sim 57M$ posts from $\sim 2M$
unique pages and groups, generating $\sim 8B$ interactions. The result of the
data collection process is described in Table 1.

| Total News Articles
---
from GDELT
| Total Posts
---
from Facebook
| Total
---
Interactions
| Total Groups
---
and Pages
| Number of Topics
---
Collected
Period
79 650 | 57 031 026 | 8 015 177 602 | 2 224 430 | 296 | 1/1/2018 - 13/5/2022

Table 1: Data Breakdown of the study, including the total amount of news
articles and posts collected from GDELT and Facebook respectively, together
with the number of topics and the analysis period..

#### Topic Categorization

To provide a correspondence between topics and their area of interest, we
performed a categorization activity under the following labels: Art-Culture-
Sport (ACS), Economy, Environment, Health, Human Rights, Labor, Politics,
Religion, Social and Tech-Science. Three human labellers carried out the
activity to connect topics and categories, choosing as the representative only
those categories selected by at least two of the three labellers.

### Metrics

We begin by describing a measure for fitting the cumulative engagement
evolution. Then, based on the previous step, we outline an index to evaluate
the sharpness of the topic’s diffusion. Finally, we introduce a sentiment
score to assess the topic’s controversy by using Facebook’s reactions.

#### Fitting cumulative engagement evolution

The diffusion of new ideas has been widely studied in the past [40, 41, 42,
43, 44, 45], indicating how the logistic function can effectively model the
diffusion of innovations. Therefore, to model the evolution of the engagement
received by posts, we fit the cumulative distribution of the overall
engagement ( i.e., the number of likes, shares and comments) over time
employing a function $f_{\alpha,\beta}(t)$, with $\alpha,\beta\in\mathbb{R}$,
defined as

$f_{\alpha,\beta}(t)=\frac{1}{1+e^{-\alpha\left(t-\beta\right)}}.$ (1)

From a mathematical point of view, Eq. 1 defines a general sigmoid function
that depends on the parameters $\alpha$ and $\beta$. The $\alpha$ parameter
represents the slope of the function, describing the steepness of the
engagement evolution. On the other hand, $\beta$ is the point at which the
function reaches the value $0.5$ and quantifies the time required for a topic
to reach half its total interactions.

(a)

(b)

(c)

(d)

Figure 2: Representation of a sample of four topics employing their normalized
cumulative evolution of engagements and fittings. The incidence of the
$\alpha$ parameter can be observed in the sharpness of the fitting curves. The
$\beta$ parameter instead regulates the shift of the function through the $x$
axis: the higher its value, the higher the delay from $t_{0}$ where the
sigmoid produces its increment.

To provide a representation of the impact that $\alpha$ and $\beta$ can have
in topic engagement evolution, Fig. 2 displays four topics with peculiar
configurations. Fig. 2(a) shows a sigmoid in which the high values of $\alpha$
and $\beta$ produce a sharp increment relatively far from $t_{0}$. Such
behaviour corresponds to those topics that require some time before gaining
resonance with the public. Fig. 2(b) instead provides a fit where the sigmoid
produces low values for $\alpha$ and $\beta$, resulting in a smoother
increment in the proximity of $t_{0}$ than the one described in Fig.2(a).
Finally, Fig. 2(c) and 2(d) provide an example of how two curves that share
similar values of $\beta$ parameters can have a different evolution of their
increase by slightly modifying the values for $\alpha$ parameter.

#### Speed Index

To model the evolution of a topic by taking into account the joint
contribution of $\alpha$ and $\beta$ parameters, we define a measure called
the Speed Index $SI(f_{\alpha,\beta})$ as

$SI(f_{\alpha,\beta})=\frac{\int_{0}^{T}f_{\alpha,\beta}(t)dt}{T},$ (2)

where $T$ represents the time of the last observed value for
$f_{\alpha,\beta}(t)$. Note that $SI$ is the mean integral value of
$f_{\alpha,\beta}$, i.e. the normalised area under the curve of
$f_{\alpha,\beta}$ (therefore $SI(f_{\alpha,\beta})\in[0,1]$). The assumption
in the definition of this function relies on the fact that high-speed values
are obtained by sigmoids that reach the plateau in a short time, as the
behaviour represented in Fig. 2(b).

#### Love-Hate Score

To quantify the level of sentiment that a Facebook post produces, we define a
measure of controversy called Love-Hate Score $LH(i)\in\left[-1,1\right]$ as

$LH(i)=\frac{l_{i}-h_{i}}{l_{i}+h_{i}},$ (3)

where $h_{i}$ and $l_{i}$ are respectively the total number of Angry and Love
reactions collected by a post $i$. A value of $LH$ equal to $-1$ indicates
that the post received only Angry reactions from the users, while a value
equal to $1$ indicates that the post received only $Love$ reactions.

## Results and Discussion

### Quantifying topic resonance

We first provide a quantitative assessment of the topics’ resonance on social
media. To do so, we perform a Non-linear Least Squares (NLS) regression by
fitting the sigmoid function $f_{\alpha,\beta}(t)$ to the cumulative evolution
of the engagement for each topic.

Figure 3: Joint distribution of $\alpha$ and $\beta$ parameters obtained from
the NLS regression for each topic. We observe that topics are generally
characterized by values of $\alpha$ and $\beta$, which explains how user
interest in a topic does not increase all of a sudden but is the result of a
process that evolves over time.

The distribution of the $\alpha$ parameter provided in Fig. 3 describes how
the majority of topics have a value of $\alpha$ belonging to the
$\left[0,0.0047\right]$ interval. This result demonstrates how user interest
in a topic does not suddenly increase but results from a long-term process.
The distribution of the $\beta$ parameter, instead, describes a prevalence of
topics in the $\left[600,1000\right]$ interval, identifying the tendency of
topics to become a matter of interest with some delay w.r.t the first post
covering them.

### Evaluating the relationship between topic resonance and controversy

To quantify the interplay between user interest in a topic and the controversy
it produces, we compute the Spearman correlation between the Speed Index and
the Love-Hate (LH) Score for each topic. Results from the upper panel of Fig.
4 show a general negative tendency of users to react with adverse sentiment
when a topic gains engagement faster ($\rho=-0.26$), leaving positive
reactions to those topics that require time to gain resonance. Results
described in the lower panel of Fig. 4 provide further characterization of the
interplay between the Speed Index and the Love-Hate score after classifying
the topics according to the four most frequent categories analyzed, i.e.,
Politics, Labor, Human Rights and Health. We observe how the Politics and
Health categories have the lowest correlation scores ($\rho=-0.36$ and
$\rho=-0.45$), providing further evidence of their intrinsic polarizing
attitude (see Table 5 for the complete list of correlation coefficients).
Furthermore, the correlation between $\alpha$ and LH Score produces similar
results as with the Speed Index (see Fig. 5 in SI for more details).

Figure 4: Upper panel: correlation between $SI$ and $LH$ score for each of the
topics identified. Lower panel: correlation between $SI$ and $LH$ score for
the top $4$ most frequent topics. Overall, we observe how users react
negatively as topics become sharply viral.

### Assessing the differences of engagement behaviors across topic categories

To conclude our analysis, we investigate the differences in the evolution of
engagement across topic categories. In particular, for each parameter
distribution ($\alpha$, $\beta$ and $SI$), we apply a two-tailed Mann–Whitney
U test [46] to each pair of parameters. Table 2 provides the percentages of
the significant p-values for the four parameters. Due to the necessity to
perform multiple tests, we apply a Bonferroni correction to our standard
significance level of $0.05$, leading to reject the null hypothesis if the
p-value $p<0.001$. Our results show that the resulting p-values from the tests
do not lead to rejecting the null hypothesis. Such a result corroborates the
hypothesis that, on average, users are characterized by homogeneous engagement
patterns that are not influenced by the consumed topic. We further extend the
statistical assessment by performing the same test between Love-Hate Score
distributions of the different categories.

| $\alpha$ | $\beta$ | Speed Index | Love-Hate
---|---|---|---|---
<0.001 | 2.22% | 0% | 0% | 20%
>0.001 | 97.78% | 100% | 100% | 80%
Table 2: Percentage of p-values resulting from the two-sided Mann–Whitney U
test between each category employing their $\alpha$, $\beta$, Speed Index and
Love-Hate Score.

Conversely to engagement evolution results, the topic’s category explains
differences in the sentiment of reactions in 20% of cases. Such findings
reveal that some categories are composed of significantly more negative and
controversial topics, indicating how elicited reactions vary according to
specific subjects. Understanding that some of them are more prone to induce
negative feedback from users could be a proxy to introduce their related
topics in the online debate.

## Conclusions

In this work, we perform a quantitative analysis of user interest on a total
of $\sim 57M$ Facebook posts referring to $\sim 300$ different topics ranging
from $2018$ to $2022$. We initially quantify the distribution of topics’
resonance throughout the analysis. Then, we evaluate the relationship between
engagement and controversy. Ultimately, we assess the differences in
engagement across different categories of topics. Our findings show that, on
average, user interest in topics does not increase exponentially right after
their appearance but, instead, it grows steadily until it reaches a saturation
point. From a sentiment perspective, topics that gained resonance right after
their initial appearance are more likely to collect negative/controversial
reactions, whilst topics which are more steady in their growth tend to attract
positive user interactions. This result provides evidence about how
recommendation algorithms should introduce topics adequately since sudden
rises in topic resonance tend to reinforce polarization mechanisms. Finally,
we find no statistical difference between user interest across different
categories of topics, providing evidence that, on a relatively large time
window, the evolution of engagement with posts is primarily unrelated to their
subject. On the contrary, we observe differences in the sentiment generated by
the different topics, providing evidence of how polarisation drives people to
perceive the piece of content they consume online in different ways, according
to their framing and system of beliefs.

User interest and engagement evolution in the online debate are both aspects
of human behaviour on social media whose underlying dynamics still need to be
discovered from an individual point of view. Our findings provide an aggregate
perspective of the interplay between major emerging behavioral dynamics and
topics’ lifetime progression, deepening the relationship between diffusion
patterns and users’ reactions. Understanding that topics with an early burst
in virality are associated with primarily adverse reactions from users sheds
light on their tendency to react instinctively to new content. This approach
enables the identification of highly polarizing topics, since their initial
stage of diffusion, by observing the heterogeneity of users’ reactions. The
following study presents some limitations. In data collection, CrowdTangle
provides only posts from public Facebook pages with more than 25K Page Likes
or Followers, public Facebook groups with at least 95K members, all US-based
public groups with at least 2K members, and all verified profiles. These
restrictions affected our datasets’ sample and our findings’ generality.
Moreover, we did not have access to removed posts, groups, and pages, which
could have been a meaningful proxy to characterize the attention dynamics of
retracted content. Finally, since Crowdtangle does not provide information
about users interacting with posts, we cannot assess their engagement from an
individual perspective and model the possible relationship between users and
topics employing a network approach.

Future works may extend the application of the proposed methodology on
additional social media platforms to assess the role of the algorithms in the
attention economy. Researchers may also take advantage of the extensions to
further platforms by assessing the attention dynamics of users concerning
specific topics.

##  1 Acknowledgements

We acknowledge the 100683EPID Project “Global Health Security Academic
Research Coalition” SCH-00001-3391.

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## Supporting Information

### List of topics employed

Topic Keywords | First Post Date | Last Post Date | Categories
---|---|---|---
Amyotrophic_lateral_sclerosis | 2018-01-02 | 2021-12-31 | Social, Health
DeleteUber | 2018-01-01 | 2021-12-19 | Labor, Social
Roy_Moore_sexual_misconduct | 2018-01-02 | 2021-12-13 | Human_Rights, Politics, Social
abilene_zoo | 2018-01-08 | 2022-01-01 | Art_Culture_Sport, Environment
abu_sayyaf | 2018-01-02 | 2021-12-31 | Human_Rights, Politics, Religion
action_news_jax | 2018-01-01 | 2021-12-31 | Art_Culture_Sport
afghan_refugees | 2018-01-01 | 2021-12-31 | Human_Rights
afghanistan_pakistani_militant | 2018-01-02 | 2021-12-31 | Human_Rights, Politics, Religion
afghanistan_war | 2018-01-02 | 2022-01-01 | Human_Rights, Politics, Religion
afp_paedophile_ring | 2018-08-15 | 2021-11-02 | Human_Rights
agent_skripal_spy | 2018-03-05 | 2021-12-29 | Politics
aids_hiv | 2018-01-02 | 2021-12-31 | Social, Health
al_aqsa_jerusalem_raid | 2018-01-15 | 2021-12-20 | Human_Rights, Politics, Religion, Social
alaska_pipeline | 2018-01-02 | 2021-12-31 | Economy, Environment
alex_jones | 2018-01-02 | 2021-12-31 | Art_Culture_Sport, Politics, Social
alshabab_mogadishu_somalia | 2018-01-24 | 2021-12-31 | Human_Rights, Politics, Religion
aluminium_steel_tariffs | 2018-01-11 | 2021-12-31 | Economy, Labor, Politics
andhra_pradesh_uttarandhra | 2018-02-05 | 2021-12-03 | Economy, Politics, Social
animal_conservation | 2018-01-01 | 2021-12-31 | Environment
animal_cruelty | 2018-01-02 | 2021-12-31 | Environment, Health
animal_sanctuary_tiverton | 2018-03-19 | 2021-12-18 | Environment, Labor
antarctic_ice_melting | 2018-01-02 | 2021-12-31 | Environment, Social
antisemitic_jewish_orthodox | 2018-02-02 | 2021-12-26 | Human_Rights, Religion, Social
apc_pdp_sheriff | 2018-01-03 | 2021-12-30 | Politics
armenia_azerbaijan_border | 2018-01-09 | 2021-12-31 | Politics, Social
arvind_kejriwal | 2018-01-02 | 2020-12-31 | Human_Rights, Politics, Social
ashland_fundraising | 2018-01-03 | 2021-12-31 | Art_Culture_Sport, Economy, Social
asian_hate | 2018-01-01 | 2021-12-31 | Human_Rights, Social
aung_san_suu_kyi | 2018-01-02 | 2021-12-31 | Human_Rights, Politics, Social
australian_refugees | 2018-01-02 | 2022-01-01 | Human_Rights, Labor, Social
baghdad_shiites | 2018-01-04 | 2021-11-29 | Religion, Social
ballistic_missile_test | 2018-01-01 | 2021-12-31 | Environment, Politics, Social, Tech_Sci
band_debut_album | 2018-01-01 | 2021-12-31 | Art_Culture_Sport
benghazi_libya_militias | 2018-01-04 | 2021-12-21 | Human_Rights, Politics, Religion
bilateral_cooperation | 2018-01-01 | 2021-12-31 | Economy, Politics
birds_invasive_population_species_conservation | 2018-01-05 | 2021-12-31 | Environment
black_racism | 2018-01-01 | 2021-12-31 | Human_Rights, Social
blacklivesmatter | 2019-05-21 | 2021-12-31 | Human_Rights, Social
blue_whale_challenge | 2018-01-01 | 2021-12-31 | Social, Health
boat_sinks_die | 2018-01-01 | 2021-12-25 | Human_Rights, Social
boeing_737_max_crash | 2018-01-01 | 2021-12-31 | Social
boko_haram | 2018-01-02 | 2022-01-01 | Human_Rights, Politics, Religion
bollywood_celebrities | 2018-01-01 | 2021-12-31 | Art_Culture_Sport
bolsonaro_brazil | 2018-01-03 | 2021-12-31 | Human_Rights, Politics, Social
bomber_commits_suicide | 2018-01-24 | 2021-12-14 | Social
boris_hunt_tory_debate | 2018-01-31 | 2021-09-04 | Politics, Social
boris_johnson | 2018-01-01 | 2021-12-31 | Politics
bowe_bergdahl | 2018-01-03 | 2021-12-26 | Human_Rights
brain_cells_tumour | 2018-01-03 | 2021-12-31 | Social, Tech_Sci, Health
breast_cancer | 2018-01-01 | 2021-12-31 | Social, Health
britain_bridge_collapse | 2018-01-03 | 2021-12-23 | Art_Culture_Sport, Environment
bsf_jammu_kashmir | 2018-01-02 | 2021-12-31 | Politics
buckingham_palace | 2018-01-02 | 2021-12-31 | Art_Culture_Sport, Politics, Social
burqa | 2018-01-01 | 2021-12-31 | Human_Rights, Religion, Social
bus_accident | 2018-01-01 | 2021-12-31 | Labor, Social

california_wildfire | 2018-01-01 | 2021-12-31 | Environment
---|---|---|---
cameron_outcome_referendum | 2018-01-02 | 2021-12-20 | Politics, Social
capital_punishment | 2018-01-01 | 2021-12-31 | Human_Rights, Social
cathedral_notre_dame | 2018-01-02 | 2021-12-31 | Art_Culture_Sport, Environment, Social
charlie_hebdo | 2018-01-02 | 2021-12-31 | Art_Culture_Sport, Human_Rights, Politics, Religion, Social
charlottesville_rally_unite | 2018-01-02 | 2021-12-31 | Human_Rights, Social
chemtrails | 2018-01-01 | 2021-12-31 | Environment, Social, Tech_Sci
climate_warming | 2018-01-01 | 2021-12-31 | Environment, Social
co2_emissions | 2018-01-01 | 2021-12-31 | Environment, Politics, Tech_Sci
coach_k | 2018-01-01 | 2021-12-31 | Art_Culture_Sport
colombia_farc | 2018-01-01 | 2021-12-31 | Politics
colorado_shooting | 2018-01-02 | 2021-12-31 | Social
confederate_statue_removed | 2018-01-04 | 2021-12-31 | Art_Culture_Sport, Human_Rights, Social
contest_nobel_prize_winner | 2018-01-17 | 2021-12-16 | Art_Culture_Sport, Tech_Sci
correctional_prisons | 2018-01-02 | 2021-12-31 | Human_Rights
crypto_currency_exchange | 2018-01-02 | 2021-12-31 | Economy, Labor, Tech_Sci
cuban_embargo | 2018-01-02 | 2021-12-31 | Economy, Labor, Politics
cultural_heritage | 2018-01-02 | 2021-12-31 | Art_Culture_Sport, Environment, Human_Rights, Religion, Social
cyberbullying | 2018-01-01 | 2021-12-31 | Social, Tech_Sci
cybersecurity | 2018-01-01 | 2021-12-31 | Politics, Social, Tech_Sci
cybersquatting | 2018-01-09 | 2021-12-23 | Economy, Labor, Social, Tech_Sci
dakota_pipeline | 2018-01-01 | 2021-12-31 | Economy, Environment
dakota_standing_rock | 2018-01-01 | 2021-12-31 | Art_Culture_Sport, Environment, Human_Rights
delhi_pollution | 2018-01-01 | 2021-12-31 | Environment, Social, Tech_Sci
democracy_threat | 2018-01-01 | 2021-12-31 | Politics, Social
democrat_min_wage | 2021-01-02 | 2021-12-31 | Economy, Human_Rights, Labor, Politics
dieselgate | 2018-01-01 | 2021-12-31 | Economy, Environment, Labor, Tech_Sci
diplomatic_immunity | 2018-01-01 | 2021-12-31 | Politics
divorce_equality | 2018-01-01 | 2021-12-31 | Economy, Politics, Social
draft_nfl | 2018-01-02 | 2021-12-31 | Art_Culture_Sport
duncan_dallas_ebola | 2018-04-12 | 2021-08-01 | Social, Health
duterte_philippines | 2018-01-02 | 2021-01-01 | Human_Rights, Politics, Social
e-cigarettes | 2018-01-02 | 2021-12-31 | Economy, Environment, Tech_Sci, Health
early_late_voter | 2018-01-03 | 2021-12-31 | Politics, Social
earthquake_nepal | 2019-01-02 | 2022-01-01 | Environment
efcc_alleged_fraud | 2018-01-03 | 2021-12-30 | Economy, Labor
el_chapo_guzman | 2018-01-02 | 2021-12-31 | Economy, Social, Health,
elon_musk_tesla | 2018-01-02 | 2021-12-31 | Economy, Environment, Labor, Tech_Sci
endangered_species | 2018-01-01 | 2021-12-31 | Environment, Tech_Sci, Health
epa_effort | 2018-01-03 | 2021-12-30 | Environment, Social, Health
erdogan_coup_d_etat_attempt | 2018-01-21 | 2021-12-06 | Politics, Social,
erdogan_turkey | 2018-01-01 | 2021-12-31 | Human_Rights, Politics
eruption_volcanic_ash | 2018-01-02 | 2021-12-31 | Environment
european_commission | 2018-01-01 | 2021-12-31 | Economy, Labor, Politics, Social
fact-checking | 2018-01-01 | 2021-12-31 | Social
factory_farming | 2018-01-01 | 2021-12-31 | Economy, Environment, Labor
fadnavis_maharashtra | 2018-01-02 | 2021-12-31 | Environment, Politics, Social
farmers_irrigation_scheme | 2018-01-02 | 2021-12-31 | Economy, Environment, Labor, Tech_Sci
fashion_runway | 2018-01-01 | 2021-12-31 | Art_Culture_Sport
fiscal_cuts | 2018-01-01 | 2021-12-31 | Economy, Labor, Politics
flat_earth | 2018-01-01 | 2021-12-31 | Environment, Social, Tech_Sci
football_galbraith | 2019-01-07 | 2021-12-27 | Art_Culture_Sport
ford_kavanaugh | 2018-02-13 | 2021-12-30 | Human_Rights, Social
forest_wildfire | 2018-01-02 | 2021-12-31 | Environment
garda_dublin | 2018-01-03 | 2021-12-31 | Labor, Social
gay_marriages_ban | 2018-01-05 | 2021-12-30 | Human_Rights, Politics, Social
gdpr | 2018-01-02 | 2022-01-01 | Human_Rights, Politics, Social
geert_wilders_netherlands | 2018-01-03 | 2021-12-05 | Human_Rights, Politics, Religion
gender_bathroom | 2018-01-01 | 2021-12-31 | Social
gender_gap | 2018-01-01 | 2021-12-31 | Economy, Human_Rights, Labor, Politics, Social

gender_identity | 2018-01-02 | 2021-12-31 | Human_Rights, Social
---|---|---|---
george_bush | 2018-01-02 | 2021-12-31 | Politics
germany_nazi_merkel | 2018-01-02 | 2021-12-24 | Human_Rights, Politics, Religion, Social
grace_mugabe | 2018-01-02 | 2021-12-30 | Economy, Environment, Human_Rights, Politics
greek_bailout_tsipras | 2018-01-08 | 2021-07-16 | Economy, Labor, Politics, Social
hackers_disinformation | 2018-01-03 | 2021-12-28 | Social, Tech_Sci
haftar_lybia | 2018-01-02 | 2021-12-31 | Politics
hajj_pilgrimage | 2018-01-02 | 2021-12-31 | Religion, Social
halifax_mass_shooting | 2018-01-25 | 2021-12-06 | Social
hamas | 2018-01-01 | 2021-12-31 | Human_Rights, Politics, Religion
harvey_weinstein_sexual_abuse | 2018-01-02 | 2021-12-31 | Art_Culture_Sport, Human_Rights
hate_speech | 2018-01-01 | 2021-12-31 | Social
hezbollah_lebanon | 2018-01-02 | 2021-12-31 | Human_Rights, Politics, Religion, Social
hijab_ban | 2018-01-01 | 2021-12-31 | Human_Rights, Religion, Social
holocaust | 2018-01-01 | 2021-12-31 | Human_Rights, Religion, Social
homeless_shelter | 2018-01-01 | 2021-12-31 | Human_Rights, Social, Health
hong_kong_protest | 2018-01-02 | 2021-12-31 | Human_Rights, Politics, Social
honolulu_civil_beat | 2021-11-02 | 2021-12-31 | Art_Culture_Sport, Labor, Politics, Social
houthi_yemen | 2018-01-02 | 2021-12-31 | Politics, Religion, Social
humanitarian_aid | 2018-01-01 | 2021-12-31 | Human_Rights
hurricane_dorian | 2021-01-02 | 2021-12-31 | Environment
hydrogen_vehicles | 2018-01-01 | 2021-12-31 | Economy, Environment, Labor, Tech_Sci
illegal_immigration | 2018-01-01 | 2021-12-31 | Human_Rights, Politics
imran_khan | 2018-01-02 | 2021-12-31 | Art_Culture_Sport, Politics, Social
india_foreign_investment | 2018-01-02 | 2021-12-31 | Economy, Labor, Politics
intensive_animal_farming | 2018-01-04 | 2021-12-31 | Economy, Environment, Labor
iran_foreign_minister_zarif | 2018-01-02 | 2021-12-13 | Politics
iraqi_kurdish_mosul | 2018-01-03 | 2021-12-27 | Politics, Religion, Social
ireland_sinn_fein | 2018-01-01 | 2021-12-31 | Politics
jakarta_flood | 2018-01-03 | 2021-12-27 | Environment
jamal_khashoggi | 2018-01-01 | 2021-12-31 | Art_Culture_Sport, Human_Rights, Politics
jeffrey_epstein | 2018-01-01 | 2021-12-31 | Human_Rights
jeremy_corbyn_labour | 2018-01-02 | 2021-12-31 | Labor, Politics, Social
john_mccain | 2018-01-01 | 2021-12-31 | Politics
joyce_marcel | 2018-01-04 | 2021-12-31 | Art_Culture_Sport
julian_assange_wikileaks | 2018-01-02 | 2021-12-31 | Art_Culture_Sport, Economy, Politics, Social
kabila_congo | 2018-01-01 | 2021-12-31 | Politics
karnataka_assembly_poll | 2018-01-01 | 2021-12-30 | Politics
kayapo | 2018-01-03 | 2021-12-31 | Environment, Human_Rights
kenney_elected_mayor_philadelphia | 2018-01-20 | 2021-12-31 | Politics, Social
kiev_donetsk_separatists | 2018-01-13 | 2021-12-31 | Politics, Social
kiir_machar_south_sudan | 2018-01-03 | 2021-12-31 | Politics
kilauea_eruption | 2018-01-01 | 2021-12-31 | Environment, Tech_Sci
kim_jong_un | 2018-01-01 | 2021-12-31 | Human_Rights, Politics
klopp_liverpool | 2018-01-02 | 2021-12-31 | Art_Culture_Sport
labor_movement | 2018-01-02 | 2021-12-31 | Economy, Labor, Politics
lahore_rape | 2018-01-01 | 2021-12-26 | Human_Rights
lee_kuala_lumpur | 2018-01-02 | 2021-12-31 | Social
legalize_prostitution | 2018-01-05 | 2021-12-30 | Social
leo_varadkar_taoiseach | 2018-01-02 | 2021-12-31 | Human_Rights, Politics, Social
lgbt_discrimination | 2018-01-02 | 2021-12-31 | Human_Rights, Social
london_mayor_re-election_bid | 2018-02-01 | 2021-11-15 | Politics, Social
louisiana_parish_arrested | 2018-01-02 | 2021-12-31 | Social
lung_cancer | 2018-01-01 | 2021-12-31 | Social, Tech_Sci, Health
macron_france | 2018-01-01 | 2021-12-31 | Politics
maduro_venezuela | 2018-01-02 | 2021-12-29 | Politics
marco_rubio_debate | 2021-01-03 | 2021-12-25 | Politics
marijuana_legalization | 2018-01-02 | 2022-01-01 | Economy, Politics, Social, Health
marine_corps | 2018-01-01 | 2021-12-31 | Labor, Social

marine_le_pen | 2018-01-02 | 2021-12-31 | Politics
---|---|---|---
mars_mission | 2018-01-01 | 2021-12-31 | Environment, Tech_Sci
mars_spacecraft_mission | 2018-01-02 | 2021-12-31 | Environment, Tech_Sci
martin_luther_king | 2018-01-02 | 2022-01-01 | Human_Rights, Politics, Social
maryam_nawaz | 2018-01-01 | 2021-12-31 | Politics
mass_shootings | 2018-01-02 | 2022-01-01 | Social
measles | 2018-01-01 | 2021-12-31 | Social, Tech_Sci, Health
meghan_harry | 2018-01-02 | 2022-01-01 | Politics, Social
merkel_germany | 2018-01-01 | 2021-12-31 | Politics
metoo | 2018-01-01 | 2021-12-31 | Human_Rights, Labor, Social
metric_tonnes_waste | 2018-01-02 | 2021-12-30 | Environment, Tech_Sci
mexican_migrants | 2018-01-01 | 2021-12-31 | Human_Rights, Politics, Social
mexico_wall | 2018-01-01 | 2021-12-31 | Human_Rights, Politics, Social
mh370 | 2019-01-02 | 2021-12-29 | Social
michael_brown_shooting | 2018-01-03 | 2021-12-31 | Human_Rights, Social
michael_cohen_lawyer | 2018-01-05 | 2021-12-31 | Politics
migration_pact | 2018-01-05 | 2021-12-31 | Human_Rights, Politics, Social
mike_pence_indiana | 2018-01-02 | 2021-12-20 | Politics
mindanao_martial_law | 2018-01-01 | 2021-12-31 | Human_Rights, Politics, Social, Health
minimum_wage | 2018-01-02 | 2021-12-31 | Economy, Human_Rights, Labor, Politics, Social
mission_moon | 2018-01-01 | 2021-12-31 | Environment, Tech_Sci
modis_narendra | 2018-01-05 | 2021-12-29 | Politics
morsi_sisi_egypt | 2018-01-09 | 2021-12-30 | Politics, Religion
mueller_probe | 2018-01-02 | 2021-12-30 | Politics
muslim_brotherhood | 2018-01-01 | 2021-12-31 | Religion
nafta_trade | 2018-01-01 | 2021-12-31 | Economy, Labor, Politics
native_american_indigenous | 2018-01-01 | 2021-12-31 | Human_Rights
natural_gas_prices | 2018-01-01 | 2021-12-31 | Economy, Environment, Labor, Politics
nauru_refugees | 2018-01-02 | 2021-12-31 | Human_Rights
nelson_bay_cup | 2018-01-13 | 2021-12-31 | Art_Culture_Sport
netanyahu | 2018-01-01 | 2021-12-31 | Politics
nicola_sturgeon_scotland_independence | 2018-01-02 | 2021-12-31 | Politics, Social
nigel_farage_ukip | 2018-01-02 | 2021-12-28 | Politics, Social
nikolas_cruz | 2018-01-09 | 2021-12-31 | Social
nitish_kumar_bihar | 2018-01-02 | 2021-12-31 | Politics
npa_rebels | 2018-01-01 | 2021-12-31 | Politics
nuclear_war | 2018-01-01 | 2021-12-31 | Environment, Politics, Social, Tech_Sci
obrador_mexico | 2018-01-01 | 2021-12-31 | Politics
ocean_fishing | 2018-01-02 | 2021-12-31 | Economy, Environment, Labor
off_peak_season_travel | 2018-01-01 | 2021-12-31 | Art_Culture_Sport, Economy, Environment
offshore_wind | 2018-01-01 | 2021-12-31 | Economy, Environment, Tech_Sci
operation_varsity_blues | 2018-06-16 | 2021-12-31 | Art_Culture_Sport, Labor, Social
opioid_drug_crisis | 2018-01-01 | 2021-12-31 | Social, Health
organ_trade | 2018-01-01 | 2021-12-31 | Economy, Human_Rights, Health
pacific_solution | 2018-01-01 | 2021-12-31 | Human_Rights, Politics
panama_papers | 2018-01-02 | 2021-12-31 | Economy
paul_manafort | 2018-08-02 | 2021-12-30 | Politics
pension_retirement_age | 2018-01-01 | 2021-12-31 | Economy, Labor, Politics, Social, Health
pkk | 2018-01-02 | 2021-12-31 | Human_Rights, Politics, Social
planned_pregnancy | 2018-01-02 | 2022-01-01 | Human_Rights, Social, Tech_Sci, Health
plastic_surgery | 2018-01-02 | 2021-12-31 | Social, Tech_Sci, Health
ponte_morandi | 2018-01-03 | 2021-12-31 | Social
portland_standoff | 2018-01-03 | 2021-12-12 | Human_Rights, Social
protest_sign_mou | 2018-01-09 | 2021-12-22 | Social
rajapaksa_sri_lanka | 2018-01-02 | 2021-12-31 | Politics
rajasthan_mps | 2018-01-07 | 2021-12-31 | Politics
rajya_sabha_elections_bjp | 2018-01-02 | 2021-12-30 | Politics, Social
rakhine_rohingya_myanmar | 2018-01-01 | 2021-12-31 | Human_Rights, Religion, Social
ramaphosa_south_africa | 2018-01-01 | 2021-12-31 | Human_Rights, Labor, Politics, Social
randolph_holhut | 2018-02-21 | 2021-09-30 | Art_Culture_Sport

ransomware | 2018-01-01 | 2021-12-31 | Economy, Tech_Sci
---|---|---|---
rauner_illinois | 2018-01-01 | 2021-12-22 | Politics
recreational_cannabis | 2018-01-01 | 2021-12-31 | Politics, Social, Health
religion_freedom | 2018-01-01 | 2021-12-31 | Human_Rights, Religion
reynolds_mourned | 2018-07-04 | 2021-12-31 | Labor, Social
rock_n_roll_savannah_marathon | 2018-01-04 | 2021-12-14 | Art_Culture_Sport, Social
roe_v._wade_case | 2018-01-02 | 2021-12-31 | Human_Rights, Social
ryanair_pilot_strike | 2018-01-04 | 2019-10-03 | Labor
salman_saudi_arabia | 2018-01-01 | 2021-12-31 | Human_Rights, Politics, Religion,
santos_colombia | 2018-01-02 | 2021-12-31 | Politics
sargsyan_armenia | 2018-01-02 | 2021-12-31 | Politics
scientist_hansen_nasa | 2018-01-06 | 2021-12-29 | Art_Culture_Sport, Environment, Labor, Tech_Sci
scott_morrison_kabul | 2018-02-18 | 2021-12-30 | Human_Rights, Politics
scott_walker_wisconsin | 2018-01-02 | 2021-12-31 | Politics
self-driving_car | 2018-01-01 | 2021-12-31 | Economy, Labor, Social, Tech_Sci
shiv_sena_maharashtra | 2018-01-02 | 2021-12-31 | Human_Rights, Politics
sirisena_sri_lanka | 2018-01-01 | 2021-12-30 | Politics
smart_cities | 2018-01-01 | 2021-12-31 | Economy, Environment, Social, Tech_Sci
smith_scandal_resigns | 2018-03-25 | 2021-11-26 | Art_Culture_Sport
snowden | 2018-01-02 | 2021-12-31 | Human_Rights, Politics, Social, Tech_Sci
society_civil_servants | 2018-01-01 | 2021-12-31 | Labor, Politics
solar_panels | 2018-01-01 | 2021-12-31 | Economy, Environment, Tech_Sci
sonia_rahul_gandhi | 2018-01-02 | 2021-12-31 | Politics
spacex_moon_mission | 2018-01-01 | 2021-12-30 | Environment, Labor, Tech_Sci
spending_cuts | 2018-01-01 | 2021-12-31 | Economy, Labor, Politics, Social
stolen_identity | 2018-01-01 | 2021-12-31 | Social
syria_kurdish_war | 2018-01-02 | 2021-12-30 | Human_Rights, Politics, Social
syrian_refugees | 2018-01-01 | 2021-12-31 | Human_Rights
tamil_ltte | 2018-01-04 | 2021-12-31 | Human_Rights, Politics, Social
telangana_rao_trs | 2018-01-02 | 2021-12-31 | Politics
theresa_may_brexit | 2018-01-02 | 2021-12-30 | Politics, Social
tiger_woods_win | 2018-01-02 | 2021-12-31 | Art_Culture_Sport
tony_abbott_malcolm_turnbull | 2018-01-02 | 2022-01-01 | Politics
tourism_boost | 2018-01-01 | 2021-12-31 | Economy, Environment, Labor
tourists_overrun | 2018-01-03 | 2021-12-31 | Environment, Social
tpp | 2018-01-02 | 2021-12-31 | Economy, Politics
truck_highway_crashes | 2018-01-02 | 2021-12-31 | Labor
trudeau_canada | 2018-01-02 | 2021-12-31 | Politics
trump_impeachment | 2018-01-01 | 2021-12-31 | Politics
tsai_ing-wen_taiwan | 2018-01-01 | 2021-12-31 | Human_Rights, Politics, Social
tshisekedi_congo | 2018-01-01 | 2021-12-31 | Politics
tupac | 2018-01-26 | 2021-12-09 | Art_Culture_Sport
uber_ride_sharing | 2018-01-02 | 2021-12-31 | Economy, Labor
uhuru_kenyatta | 2018-01-01 | 2021-12-31 | Politics
undercover_comey_fbi | 2018-02-07 | 2021-11-08 | Politics
undp_procurement | 2018-01-04 | 2021-12-31 | Economy, Human_Rights, Labor, Politics, Social
unhcr_refugees | 2018-01-01 | 2021-12-31 | Human_Rights
vatican_abuse | 2018-01-03 | 2022-01-01 | Human_Rights, Religion
white_supremacist | 2018-01-01 | 2021-12-31 | Human_Rights, Social
william_barr_attorney_general | 2018-04-30 | 2021-12-31 | Politics
william_kate_middleton | 2018-01-02 | 2021-12-31 | Politics, Social
williams_plead_guilty | 2018-01-03 | 2021-12-25 | Social
wirecard_scandal | 2019-01-02 | 2022-05-05 | Economy, Labor
women_abortion | 2018-01-02 | 2022-01-01 | Human_Rights, Social, Health
xi_jinping | 2018-01-01 | 2021-12-31 | Politics
xinhua_silk_road | 2018-01-19 | 2021-12-30 | Economy, Labor
yakubu_dogara_sacked | 2018-01-15 | 2021-12-05 | Politics
yanukovych_crimea | 2018-01-03 | 2021-12-30 | Politics
ywca | 2018-01-02 | 2021-12-31 | Human_Rights, Religion, Social
zayed_uae | 2018-01-01 | 2021-12-31 | Economy, Politics, Social
zika_virus | 2018-01-02 | 2021-12-31 | Social, Health
zuma_south_africa | 2018-01-01 | 2021-12-31 | Human_Rights, Politics, Social

Table 3: List of terms employed to perform the research for each topic
together with the first and last date when a post related to each topic was
found.

### Evaluating the relationship between topic resonance and controversy

Figure 5: Correlation between $\alpha$ and $LH$ score for each of the topics
identified

### Goodness of the fitting procedure

We fit the cumulative evolution of engagement for the topics in Section List
of topics employed with the function $f_{\alpha,\beta}$. The fitting procedure
produces, for each topic $i$, the tuples
$\left(\hat{\alpha_{i}},\hat{\beta_{i}}\right)$ and
$\left(SE(\hat{\alpha_{i}}),SE(\hat{\beta_{i}})\right)$ containing the
estimated parameters with their standard errors, respectively. Fig. 6 provides
a joint distribution of the errors
$\left(SE(\hat{\alpha_{i}}),SE(\hat{\beta_{i}})\right)$ for each topic in
relationship with the number of posts they produced. We observe how
$SE(\hat{\alpha_{i}})$ errors follow a log-normal distribution, while
$SE(\hat{\beta_{i}})$ errors have a normal one. We can observe a reduction in
the errors for both parameters as the number of posts per topic increases. We
formerly assess such relationship by computing a Spearman correlation
coefficient between each standard error and the number of posts per topic,
obtaining a value of $\rho(SE(\hat{\alpha_{i}}),posts_{i})=-0.44$ and
$\rho(SE(\hat{\beta_{i}}),posts_{i})=-0.25$. We can therefore conclude that
our fitting procedure provides results with a reducing error as the number of
observations increases.

Figure 6: Joint distribution of the errors $SE(\hat{\alpha_{i}})$ and
$SE(\hat{\beta_{i}})$ for each topic $i$, whose cumulative curve was estimated
by means of $f_{\alpha,\beta}$. The colour of each point represent the number
of posts produced by topic $i$.

### Assessing the differences of engagement behaviors across topic categories

Category | $\alpha$ | $\beta$ | SI
---|---|---|---
Art_Culture_Sport | 0.043 (0.1647) | 693.8 (236) | 0.49 (0.13)
Economy | 0.0045 (0.0022) | 752.36 (241.45) | 0.48 (0.14)
Environment | 0.0215 (0.1214) | 761.41 (207.81) | 0.47 (0.11)
Human_Rights | 0.0244 (0.113) | 765.48 (223.24) | 0.47 (0.14)
Labor | 0.0137 (0.0618) | 715.41 (286.41) | 0.49 (0.15)
Politics | 0.0192 (0.0953) | 711.78 (243.12) | 0.5 (0.15)
Religion | 0.0405 (0.1906) | 786.5 (184.07) | 0.46 (0.12)
Social | 0.024 (0.1182) | 728.58 (204.3) | 0.49 (0.12)
Tech_Sci | 0.004 (0.0013) | 801.76 (187.67) | 0.46 (0.11)
Health | 0.0045 (0.0016) | 692.03 (128.7) | 0.52 (0.08)

Table 4: Summary of $\alpha$, $\beta$ and Speed Index mean values (and SD) per topic category.  Category | Rho | p.value
---|---|---
All | -0.26 | 0
Art_Culture_Sport | -0.2 | 0.2631
Economy | -0.2 | 0.1665
Environment | -0.21 | 0.1379
Human_Rights | -0.29 | 0.0067
Labor | -0.35 | 0.0162
Politics | -0.36 | 0
Religion | -0.12 | 0.5308
Social | -0.23 | 0.0057
Tech_Sci | -0.21 | 0.2256
Health | -0.45 | 0.0267
Table 5: Spearman’s Rho between Speed and Love-Hate Score per category (CI =
0.95). For readability, 0 represents values lower than 0.0001.

| A_C_S | Econ | Env | H_R | Labor | Politics | Religion | Social | Tech | Health
---|---|---|---|---|---|---|---|---|---|---
A_C_S |  | 0.3621 | 0.9226 | 0.3671 | 0.7594 | 0.5024 | 0.8931 | 0.8646 | 0.1195 | 0.9688
Econ | 0.3621 |  | 0.2994 | 0.0104 | 0.4292 | 0.0146 | 0.3788 | 0.0793 | 0.3247 | 0.3496
Env | 0.9226 | 0.2994 |  | 0.1287 | 0.9135 | 0.2025 | 0.992 | 0.5873 | 0.0546 | 0.9688
H_R | 0.3671 | 0.0104 | 0.1287 |  | 0.134 | 0.6761 | 0.2437 | 0.2236 | 0.0009 | 0.2014
Labor | 0.7594 | 0.4292 | 0.9135 | 0.134 |  | 0.1818 | 0.8575 | 0.4937 | 0.0874 | 0.9322
Politics | 0.5024 | 0.0146 | 0.2025 | 0.6761 | 0.1818 |  | 0.335 | 0.3465 | 0.0018 | 0.2755
Religion | 0.8931 | 0.3788 | 0.992 | 0.2437 | 0.8575 | 0.335 |  | 0.6836 | 0.0976 | 0.9347
Social | 0.8646 | 0.0793 | 0.5873 | 0.2236 | 0.4937 | 0.3465 | 0.6836 |  | 0.011 | 0.6151
Tech | 0.1195 | 0.3247 | 0.0546 | 0.0009 | 0.0874 | 0.0018 | 0.0976 | 0.011 |  | 0.08
Health | 0.9688 | 0.3496 | 0.9688 | 0.2014 | 0.9322 | 0.2755 | 0.9347 | 0.6151 | 0.08 |

Table 6: p-values of the two-tailed Mann–Whitney U tests performed on the
average $\alpha$ parameter value between categories (CI = 0.95). Bold values
represent the value for which the null hypothesis was rejected.

| A_C_S | Econ | Env | H_R | Labor | Politics | Religion | Social | Tech | Health
---|---|---|---|---|---|---|---|---|---|---
A_C_S |  | 0.1688 | 0.171 | 0.2124 | 0.5789 | 0.7218 | 0.2055 | 0.5985 | 0.0532 | 0.8082
Econ | 0.1688 |  | 0.952 | 0.7537 | 0.6159 | 0.1358 | 0.9875 | 0.181 | 0.5199 | 0.0567
Env | 0.171 | 0.952 |  | 0.718 | 0.6412 | 0.0932 | 0.9759 | 0.1386 | 0.4838 | 0.0315
H_R | 0.2124 | 0.7537 | 0.718 |  | 0.6522 | 0.1274 | 0.7229 | 0.1808 | 0.2939 | 0.0599
Labor | 0.5789 | 0.6159 | 0.6412 | 0.6522 |  | 0.5506 | 0.5529 | 0.7086 | 0.2948 | 0.2793
Politics | 0.7218 | 0.1358 | 0.0932 | 0.1274 | 0.5506 |  | 0.1676 | 0.7814 | 0.0274 | 0.3969
Religion | 0.2055 | 0.9875 | 0.9759 | 0.7229 | 0.5529 | 0.1676 |  | 0.1816 | 0.5318 | 0.0418
Social | 0.5985 | 0.181 | 0.1386 | 0.1808 | 0.7086 | 0.7814 | 0.1816 |  | 0.0338 | 0.2407
Tech | 0.0532 | 0.5199 | 0.4838 | 0.2939 | 0.2948 | 0.0274 | 0.5318 | 0.0338 |  | 0.0076
Health | 0.8082 | 0.0567 | 0.0315 | 0.0599 | 0.2793 | 0.3969 | 0.0418 | 0.2407 | 0.0076 |

Table 7: p-values of the two-tailed Mann–Whitney U tests performed on the
average $\beta$ parameter value between categories (CI = 0.95). Bold values
represent the value for which the null hypothesis was rejected.

| A_C_S | Econ | Env | H_R | Labor | Politics | Religion | Social | Tech | Health
---|---|---|---|---|---|---|---|---|---|---
A_C_S |  | 0.4121 | 0.3312 | 0.3731 | 0.5789 | 0.869 | 0.4838 | 0.8942 | 0.1862 | 0.4756
Econ | 0.4121 |  | 0.9253 | 0.9877 | 0.9856 | 0.2284 | 0.9212 | 0.3115 | 0.5664 | 0.0496
Env | 0.3312 | 0.9253 |  | 0.8325 | 0.908 | 0.1166 | 0.9037 | 0.1748 | 0.6437 | 0.0223
H_R | 0.3731 | 0.9877 | 0.8325 |  | 0.9216 | 0.1215 | 0.9618 | 0.2021 | 0.5007 | 0.0447
Labor | 0.5789 | 0.9856 | 0.908 | 0.9216 |  | 0.3042 | 0.8918 | 0.4106 | 0.5814 | 0.0888
Politics | 0.869 | 0.2284 | 0.1166 | 0.1215 | 0.3042 |  | 0.2611 | 0.7012 | 0.0617 | 0.3407
Religion | 0.4838 | 0.9212 | 0.9037 | 0.9618 | 0.8918 | 0.2611 |  | 0.3424 | 0.6332 | 0.0733
Social | 0.8942 | 0.3115 | 0.1748 | 0.2021 | 0.4106 | 0.7012 | 0.3424 |  | 0.0784 | 0.1822
Tech | 0.1862 | 0.5664 | 0.6437 | 0.5007 | 0.5814 | 0.0617 | 0.6332 | 0.0784 |  | 0.0103
Health | 0.4756 | 0.0496 | 0.0223 | 0.0447 | 0.0888 | 0.3407 | 0.0733 | 0.1822 | 0.0103 |

Table 8: p-values of the two-tailed Mann–Whitney U tests performed on the
average Speed Index between categories (CI = 0.95). Bold values represent the
value for which the null hypothesis was rejected.

| A_C_S | Econ | Env | H_R | Labor | Politics | Religion | Social | Tech | Health
---|---|---|---|---|---|---|---|---|---|---
A_C_S |  | 0.0369 | 0.2472 | 0.0001 | 0.009 | 0.0001 | 0.0048 | 0.0005 | 0.3624 | 0.0656
Econ | 0.0369 |  | 0.1372 | 0.0023 | 0.4167 | 0.0089 | 0.0792 | 0.0501 | 0.0777 | 0.9081
Env | 0.2472 | 0.1372 |  | 0 | 0.0238 | 0 | 0.004 | 0.0003 | 0.7374 | 0.1817
H_R | 0.0001 | 0.0023 | 0 |  | 0.0401 | 0.4758 | 0.5167 | 0.1535 | 0 | 0.0275
Labor | 0.009 | 0.4167 | 0.0238 | 0.0401 |  | 0.107 | 0.3184 | 0.3601 | 0.0121 | 0.5761
Politics | 0.0001 | 0.0089 | 0 | 0.4758 | 0.107 |  | 0.9014 | 0.4024 | 0 | 0.0708
Religion | 0.0048 | 0.0792 | 0.004 | 0.5167 | 0.3184 | 0.9014 |  | 0.7623 | 0.0013 | 0.1672
Social | 0.0005 | 0.0501 | 0.0003 | 0.1535 | 0.3601 | 0.4024 | 0.7623 |  | 0.0002 | 0.1763
Tech | 0.3624 | 0.0777 | 0.7374 | 0 | 0.0121 | 0 | 0.0013 | 0.0002 |  | 0.1064
Health | 0.0656 | 0.9081 | 0.1817 | 0.0275 | 0.5761 | 0.0708 | 0.1672 | 0.1763 | 0.1064 |

Table 9: p-values of the two-tailed Mann–Whitney U tests performed on the average Love-Hate Score between categories (CI = 0.95). Bold values represent the value for which the null hypothesis was rejected. | A_C_S | Env | Tech | Politics | Social | H_R
---|---|---|---|---|---|---
A_C_S |  |  |  | 0.0001 | 0.0003 | 0
Env |  |  |  | 0 | 0.0002 | 0
Tech |  |  |  | 0 | 0.0001 | 0
Politics | 0.9999 | 1 | 1 |  |  |
Social | 0.9998 | 0.9998 | 0.9999 |  |  |
H_R | 1 | 1 | 1 |  |  |
Table 10: p-values of Mann–Whitney U test on LH mean value between categories
for which the null hypotesis was rejected in Table 9 (H1: $\mu_{r}>\mu_{c}$,
where r and c represent row and column category; Conf. Level = 0.95). Bold
values represent the value for which the null hypothesis was rejected.