# Characterizing Fake News Targeting Corporations

Ke Zhou1,2, Sanja Šćepanović1, Daniele Quercia1,3

###### Abstract

Misinformation proliferates in the online sphere, with evident impacts on the
political and social realms, influencing democratic discourse and posing risks
to public health and safety. The corporate world is also a prime target for
fake news dissemination. While recent studies have attempted to characterize
corporate misinformation and its effects on companies, their findings often
suffer from limitations due to qualitative or narrative approaches and a
narrow focus on specific industries. To address this gap, we conducted an
analysis utilizing social media quantitative methods and crowd-sourcing
studies to investigate corporate misinformation across a diverse array of
industries within the S&P 500 companies. Our study reveals that corporate
misinformation encompasses topics such as products, politics, and societal
issues. We discovered companies affected by fake news also get reputable news
coverage but less social media attention, leading to heightened negativity in
social media comments, diminished stock growth, and increased stress mentions
among employee reviews. Additionally, we observe that a company is not
targeted by fake news all the time, but there are particular times when a
critical mass of fake news emerges. These findings hold significant
implications for regulators, business leaders, and investors, emphasizing the
necessity to vigilantly monitor the escalating phenomenon of corporate
misinformation.

##  1 Introduction

Emerging research in economics and political science has raised concerns about
the erosion of democracy by misinformation on social media (Allcott and
Gentzkow 2017). Similarly, concerns about misinformation effects on public
health are increasing (Naeem, Bhatti, and Khan 2021; Melchior and Oliveira
2022). Yet another sphere experiencing threat due to misinformation is
corporations and capital markets (Adriani 2019; Fox 2020; Jahng 2021). There
are various types of corporate fake news depending on whether the news content
has the intention to deceive and harm (Park et al. 2020). When fake news has a
low intention to harm and deceive, the news contents might just provide
inaccurate information due to honest mistakes or negligence (e.g., inaccurate
product release leakage). However, some fake news has the intention to harm
the company but not to deceive, i.e., promoting factual but negative news used
to inflict harm on the competing organization (e.g., layoffs), while others
focus on both deceiving and harming, i.e., manipulation of information (e.g.,
company executive scandal) that purposefully aims to mislead and misinform the
audience/customers. All those types of corporate fake news may have an impact
to different extents on a company’s reputation and its capital markets.
Research on corporate fake news is limited due to a lack of data,
incorporating narrative (Adriani 2019; Camacho and LOZANO 2020), qualitative
(Serazio 2021), and survey-based approaches (Cheng and Chen 2020; Bronnenberg,
Dubé, and Sanders 2020), along with a focus on small parts of the corporate
world (Kogan, Moskowitz, and Niessner 2019; Chung, Zhang, and Pan 2022).

This study aims to characterize and provide empirical evidence of the
determinants of corporate fake news across S&P 500 companies, using a large-
scale quantitative analysis on social media data and crowd-sourced validation.
The study addresses three research questions (RQs):

_(RQ1)_ How should corporate fake news articles be categorized?

_(RQ2)_ Which companies are targeted by fake news?

_(RQ3)_ When are companies targeted by fake news?

To answer those research questions, our study makes four main contributions:

  * •

By combining social media posts from Reddit, fake news links, company stock
performance, and company reviews from Glassdoor, we _created a unique dataset_
for investigating corporate misinformation. We _developed ten metrics_ to
capture the characteristics of companies and the misinformation they were
subject to (§3).

  * •

We _conducted a user study to fully categorize the news articles_ mentioning
companies in the context of misinformation. We found that a two-level taxonomy
best describes these misinformation articles and its first level includes
products, politics, corporate affairs, societal issues, and brand. Fake news
articles mentioning high-growth companies tend to be about products and
societal issues, while those mentioning limited-growth companies tend to be
about politics (§4, _RQ1_).

  * •

We _characterized the relationship between misinformation and companies_. We
found that companies heavily targeted by fake news often receive coverage from
reputable news publishers but garner less attention on social media. However,
this not only leads to more negative sentiments in public comments on social
media but also links to decreased stock market growth, as well as increased
stress indicators among employee reviews within these targeted companies.

  * •

We found a company is not targeted by fake news all the time – there are
particular times in which a critical mass of fake news about the company
emerges (§6, _RQ3_).

##  2 Related Work

Misinformation is not a new phenomenon, as seen in historical instances like
propaganda during the Second World War (Fox 2020). However, today has been
dubbed a ’perfect storm’ for the proliferation of misinformation (Starbird
2021; Fox 2020; Edge 2021), influenced by various factors: (i) the opportunity
for anyone to become a content producer, facilitated by online platforms such
as blogs and social media posts; (ii) the virality of social media,
particularly for disseminating fake information (Vosoughi, Roy, and Aral
2018); and (iii) advancements in deep learning natural language and visual
content processing techniques, enabling the creation of fake or biased
content, such as deepfakes (Westerlund 2019), that is challenging to
distinguish from truthful content. Research suggests that misinformation
significantly impacts democratic and political discourse (Pickard 2019;
Garimella et al. 2018), public health and safety (Swire-Thompson and Lazer
2019; Verma et al. 2022), journalism (Waisbord 2018), and exacerbates social
divisions (Cover, Haw, and Thompson 2022), among other effects on the social
and political spheres. Another sphere in which misinformation poses a rising
threat is the corporate world (Jahng 2021; Adriani 2019). As an example,
COVID-19 has been falsely linked to 5G leading to the destruction of 5G towers
(Ahmed et al. 2020; Moshood, Shittu, and Abidin 2020).

Arguably the most important effect of fake news on companies is brand
reputation (Jahng 2021; Di Domenico and Visentin 2020; Mills and Robson 2019;
Castellani and Berton 2017). The damage to the brand reputation is considered
a risk by 98% of professionals, and as many as 80% of them had to deal with at
least one crisis resulting from fake news (Camacho and LOZANO 2020). Another
effect of corporate fake news is monetary (e.g., through market manipulation
(Lin 2016), decline in sales (Adriani 2019), or loss of share value
(Castellani and Berton 2017)). A successful misinformation campaign for
corporate giants such as Apple or Amazon could have monetary impact measured
in the billions of dollars (Adriani 2019). It has been shown in a small-scale
study (Xu 2021) that fake news perpetrators can come both from people external
to the company but also from employees themselves if they want to excerpt
pressure on the company. In addition, the company that responds promptly to
corporate misinformation can appropriately reduce the impact on its brand
reputation and stock price (Mills and Robson 2019; Bronnenberg, Dubé, and
Sanders 2020).

While initial evidence (PWC 2022) about the effects of fake news on companies
starts to mount, current research on this topic uses mostly narrative (Adriani
2019; Camacho and LOZANO 2020), qualitative (Serazio 2021), and survey-based
approaches (Cheng and Chen 2020), or focuses on a small part of the corporate
world (e.g., financial market (Kogan, Moskowitz, and Niessner 2019)).
Quantitative large-scale research is still nascent owing to the methodological
challenges in quantifying both the fake news (Al-Rawi and Fakida 2021) and
their effects on companies (Adriani 2019; Jahng 2021; Xu 2021; Di Domenico and
Visentin 2020). Our current research addresses this research gap by offering
insights into the fake news targeting S&P 500 companies shared on Reddit from
2016 to 2019. Notwithstanding the findings of a recent study (Eady et al.
2023) indicating that exposure to misinformation has limited influence on
political outcomes, it is imperative to acknowledge and discern the unique
dynamics inherent in the corporate context, where exposure to misinformation
might have more significant effects regarding internal or external corporate
reputation, and consequently stock valuation. We believe, unlike political
beliefs and attitudes that are often deeply ingrained and difficult to change,
consumers, employees and investors may be more easily swayed by false
information that impacts their perceptions of a company’s products or
services.

##  3 Data and Methods

To examine and compare the susceptibility of S&P 500 companies to
misinformation, we collated four types of data. We collected Reddit comments
mentioning those companies along with news articles (§3.1). The credibility of
these articles was evaluated utilizing established fact-checking sources
(§3.2). It is possible that a company’s likelihood of being targeted by fake
news may simply be related to its level of newsworthiness. This news coverage
may stem from an external interest in the company, such as financial
performance as measured by stock growth (§3.3), or internal factors within the
company. Internal company affairs have so far been harder to characterize. To
do so, we analyzed data from a reputable company review website, Glassdoor
(§3.4). Finally, using these four data sources, we computed ten metrics for
each company (§3.5).

###  3.1 Reddit Comments with Company News

We chose Reddit to collect public perception of news articles related to
companies. This is because: i) Reddit serves as a social news aggregation
site, ideal for studying news distribution and public response, and ii) unlike
typical social networks (e.g., Twitter), Reddit lacks friends or followers,
allowing for a more indicative reflection of mainstream news production and
consumption, free from social network biases like echo chambers or filter
bubbles. We used publicly available Pushshift API (Baumgartner et al. 2020) to
download the Reddit comments from January 2016 to December 2019. We included
all comments from all public and quarantined subreddits (e.g., r/the_Donald).
Since we studied S&P 500, we chose to focus only on the users who are from the
U.S.

Figure 1: Reddit users versus U.S. state population.

Reddit does not explicitly provide user location. However, it is possible to
obtain reliable estimates (Balsamo, Bajardi, and Panisson 2019). Starting from
a list of 2,844 location-based subreddits that map to one of the U.S. cities
or states (e.g., r/newyork, r/california), we listed the users with at least 5
comments in those subreddits. If they had posted in multiple states, we
assigned the user to the state with the majority of comments. In this way, we
placed $\sim$3M users in one of the U.S. states. The number of Reddit users
per state scaled linearly with the state’s population ($\beta$ = 1.01,
$R^{2}$=0.84) showing a good geographic representativeness (Figure 1).

Between 2016 and 2019, these users authored 1.4 billion comments. Of these
comments, 8.2M (0.6%) contained news links, of which 1.4M (16%) mentioned also
an S&P 500 company (Table 2). Those comments contained 11.3M/1.3M total/unique
links and had 262 (52.4%) of the S&P 500 companies mentioned (Wikipedia 2022).
Finally, we classified a Reddit comment as a fake news comment if it contained
a URL to a domain that appeared in the list of fake news domains, which was
defined using the credibility sources described next (§3.2). In total, there
were 12.8K fake Reddit comments mentioning S&P 500 companies. We can observe
the median number of Reddit comments per month discussing corporate fake news
is $408$ (Figure 2).

Table 1: Statistics about Reddit comments mentioning companies and news links
present in the comments.

Type of comments | # comments | # users | # total links | # unique links | # domains | # companies
---|---|---|---|---|---|---
comments by geolocated users in the U.S. | 1.4B | 3M |  |  |  |
company comments | 11.1M | 795K |  |  |  | 262
company comments with news | 1.4M | 322K | 11.3M | 1.3M | 5.1K | 208
company comments with fake news | 12.8K | 6.3K | 145.5K | 39.7K | 316 | 114

Table 2: Statistics about Glassdoor reviews of S&P 500 companies.

# total reviews | # US-based reviews | # companies | avg (min-max) rating | avg (min-max) stess score | time period
---|---|---|---|---|---
713K | 440K | 399 | 3.37 (1.62-5) | 1.11% (0-9.52%) | 2008-2020

###  3.2 News Credibility Sources

We established the credibility of news at the level of the publisher (or
domain). Specifically, all articles published on a domain deemed as fake or of
low credibility were also considered fake or not reputable. To determine
domain credibility, we employed a commonly referenced list of news domains in
misinformation research (Bozarth, Saraf, and Budak 2020). Specifically, we
labeled domains as reputable using lists compiled by Alexa (Alexa.com 2022),
Media Bias/Fact Check (MBSFC 2022), and Vargo et al.(Vargo, Guo, and Amazeen
2018). This resulted in a total of $8,900$ reputable news sites. Non-reputable
domains were labeled using Media Bias/Fact Check and four additional sources:
Zimdars list (Zimdars 2016), PolitiFact (Gillin 2018), the Daily Dot (Dot
2022), and Allcott, et al (Allcott, Gentzkow, and Yu 2019). By merging the
labels from each of these five sources, we classified a domain as fake if it
had a history of publishing fabricated news articles. This resulted in a total
of 933 fake news domains. Upon this classification, we found that 1.4 million
Reddit comments included mentions of $145.5K/39.7K$ total/unique fake news
articles, as well as mentions of $114(22.8\%)$ of the S&P 500 companies (as
outlined in Table 2).

Figure 2: Monthly # of Reddit comments containing both a fake link and a
company name (median: 408, standard deviation: 136). The peaks observed in
early 2017 and late 2018 were mainly caused by news about Google and Facebook.
They were accused of not complying with the law that requests the removal of
harmful content (Facebook) and using politically biased algorithms (Google).

###  3.3 Company Stock Market Data

We collated the S&P 500 stock market data, including the monthly adjusted
closing prices from 2016 until 2019 for each company, from the Yahoo Finance
portal (Yahoo Finance portal 2022). The stock data quantifies external
perception of the company from an investor perspective.

###  3.4 Glassdoor Company Reviews

To understand internal employees’ perception of their company, we acquired
data regarding internal corporate affairs from 440K reviews of the S&P 500
companies posted during the twelve years 2008-2020 on Glassdoor, a popular
company reviewing site (Table 2). In 2021, there were $50M$ monthly visitors
on the platform, and $70M$ reviews about $1.3M$ companies. Current and former
employees of companies describe their job experience, ranging from interviews
to salaries to workplace culture. All the reviews we collected were from
U.S.-based employees. We also obtained the employee ratings of each company.

###  3.5 Computing Company Metrics

Table 3: List of company metrics. Metric | Formula | Description | Distribution
---|---|---|---
# Reddit comments (C, T) | ${\\#\textrm{Reddit comments mentioning C during time period T}}$ | public popularity |
# Reddit comments per capita (C, T) | $\frac{\\#\textrm{Reddit comments mentioning C during time period T}}{\\#\textrm{Glassdoor reviews of C during time period T}}$ | corrected public popularity |
# reputable news (C, T) | ${\\#\textrm{reputable news articles mentioning C during time period T}}$ | newsworthiness |
external reputation score (C, T) | 1 - $\frac{\\#\textrm{negative Reddit comments mentioning C in time period T}}{\\#\textrm{total Reddit comments mentioning C in time period T}}$ |  | positive/neutral mentions
---
on social media
stock valuation (C, T) | adjusted closing prices at end of time period $T$ for company C | external investor perception |
stock growth (C) | $\frac{\textrm{stock valuation (C, 2019)}}{\textrm{stock valuation (C, 2014)}}$ | external investor perception |
Glassdoor rating (C, T) | Glassdoor overall rating of company C within time period T | internal perception |
internal employee stress score (C, T) | $\frac{\\#\textrm{Glassdoor reviews mentioning stress for company C within time period T}}{\\#\textrm{total Glassdoor reviews for company C within time period T}}$ | stress mentions by employees |
pFake (C, T) | $\frac{\\#\textrm{fake Reddit comments mentioning C during time period T}}{\\#\textrm{Reddit comments mentioning C during time period T}}$ |  | probability of C being
---
targeted by fake news
misinformation shock (C, T) |  # months with pFake(C, T_month)$>$(pFake(C, T)+2*std(pFake(C, T))) |  | number of concentrated
---
misinformation attacks

For each company, we collated ten metrics, with formulas listed in Table 3,
and described below.

#Reddit comments: we used the total number of Reddit comments discussing a
company as a proxy for its popularity among the general public. To make sure
that this measure does not capture simply the size of the company, we also
tested a corrected metric for popularity described next.

#Reddit comments per capita: we calculated the total number of Reddit comments
divided by the total number of Glassdoor reviews, which serves as a reasonable
proxy for company’s employee count.

#reputable news: we calculated the total number of news articles mentioning a
company in reputable news outlets, which generally captures a company’s
newsworthiness.

external reputation score: we computed Q-score (Freeman and Dart 1993) (one
minus the fraction of the number of comments with a negative sentiment to the
total number of comments mentioning the company) as a proxy for public
perception of a company. To measure the sentiment of a comment, we used the
Flair deep learning library (Akbik et al. 2019), and applied it to _all_ the
comments mentioning the company, and not only those with a news link.

Glassdoor rating: we calculated the average rating from the company reviews on
Glassdoor as a proxy for the internal perception, i.e., the company’s
popularity among the (former) employees.

internal employee stress score: we calculated the percentage of Glassdoor
reviews discussing stress at work as a proxy for stress mentions by employees.
To extract work stress mentions from reviews, we used the MedDL entity
extraction (Scepanovic et al. 2020), which uses contextual embeddings and a
deep BiLSTM-CRF sequence labeling architecture (we used the default parameters
as specified in (Scepanovic et al. 2020)). The model was pre-trained and
evaluated on a labeled dataset of 2K Reddit posts that have in total 4.5K
medical mentions called MedRed (Scepanovic et al. 2020). The state-of-the-art
MedDL method achieved a $F1$-score of $.85$ when extracting symptoms
(including stress mentions).

stock growth: we calculated the ratio of the stock valuation (average adjusted
closing prices) in 2019 and 2014 as a proxy for external investor perception
of the company. We chose to focus on the growth of the companies between these
two years for two reasons: (i) it encompasses the posting dates of the Reddit
comments in our data, thus providing a relevant time frame for examination.
Additionally, by selecting a stock growth starting date of 2014, two years
prior to the earliest Reddit comments in our dataset (from 2016), it allows
for the determination of the company’s growth rate as it would have been known
to Reddit users at that time; (ii) by focusing on long-term growth rather than
short-term growth, we increase the robustness of the metric to potential
influences of external events such as market manipulation or incidental
growth/decline (for 2021).

pFake: we computed the probability of news articles about the company being
fake as the ratio of fake to total Reddit comments with articles discussing
the company.

misinformation shock: we determined all the months with pFake$(C,T_{month})$
two standard deviations above its mean within the entire period $T$ for that
company $C$. Prior studies have shown that misinformation can be spread in a
coordinated manner, where several outlets cover the same fake news in a short
period of time (Ng and Taeihagh 2021; Muzykant et al. 2021). To identify such
occurrences, we identified periods of ‘misinformation shocks’ for companies,
i.e., these concentrated periods (months) when companies are targeted by a
significant number of fake news articles. These ‘misinformation shocks’
correspond to relatively large coordinated misinformation events.

We calculated each of these metrics for the entire time period $T_{all}$
between 2016 and 2019. The five metrics that had heavy-tailed distributions (#
Reddit comments, # Reddit comments per capita, # reputable news articles,
stock growth and pFake) were log-transformed in our analysis.

##  4 How to Categorize Fake News Articles?

Before understanding the determinants of corporate fake news, we would like to
first characterize them, specifically addressing (_RQ1_): _How should
corporate fake news articles be categorized?_ Previous studies have
highlighted the importance of identifying the intentions of news outlets in
the dissemination of fake news as a crucial step in combating its
proliferation (Tandoc Jr 2019; Kalsnes 2018).

To address this question, we employed a multi-faceted approach. First, we drew
upon existing literature to develop a taxonomy of fake news article
categories. This was subsequently expanded through the use of manually-labeled
annotations on a subset of articles, thereby identifying additional categories
(§4.1). Second, we conducted a crowd-sourced study in which all articles were
annotated with the identified categories during periods of misinformation
shocks (§4.2), and reported the resulting categories in §4.3.

###  4.1 Identifying Categories

Previous studies have explored the intentions and motivations behind the
creation and dissemination of fake news. For instance, Zhou and Zafarani
(2020) categorized fake news intentions as ‘mislead’, ‘entertain’, and
‘undefined’, while others like (Kalsnes 2018) classified motivations as
‘political’, ‘financial’, and ‘social’. Despite these endeavors, it remains
unclear how these general intentions can be translated into a more nuanced and
fine-grained taxonomy specifically for corporate fake news articles.

To address this gap in the literature, we conducted a qualitative study using
a subset of randomly sampled $100$ corporate fake news articles. Three
annotators were instructed to independently annotate the categories of these
articles using the intention taxonomies from prior literature (Pennycook and
Rand 2021; Lazer et al. 2018; Kalsnes 2018; Zhou and Zafarani 2020). The
annotators then discussed their labels to reach a final agreement on an
extended taxonomy.

As shown in Table 4 (Level 1), we found that articles were primarily
categorized as covering products, politics, corporate affairs, societal
issues, and brands. Additionally, we added a category of “Others” to capture
any additional categories that may arise. Within each high-level level-1
category (e.g., product), the news articles were further categorized into more
fine-grained level-2 categories (e.g., product issues relating to quality), as
shown in Table 4 (Level 2). In total, there are 19 level-2 categories.

Table 4: Companies that are targeted by fake news tend to be mentioned in
articles mainly covering products, politics, corporate affairs, societal
issues and brands. The top 3 and bottom 3 categories that are widely covered
by news outlets are in bold and italic respectively. For each category, the
top associated moral dimension (with the highest absolute morality score) is
shown in the “Morality” column.

###  4.2 Annotating Articles with Categories

We then used the manually-labelled taxonomy to label a larger subset of
$N$=$1500$ salient news articles with a crowd-sourcing approach.

Identifying articles during misinformation shocks. In order to identify
articles during periods of misinformation, a representative sample of fake
news articles was selected. Specifically, articles that covered the companies
of interest and were shared during “misinformation shocks” (as defined in §3)
were considered representative. To mitigate the potential for the most popular
companies to dominate the results, a random selection process was employed as
follows: (a) we randomly selected a company $C$; (b) we extracted a random set
of $k=50$ articles about company $C$ shared during the months of
misinformation shocks; (c) we iterated the previous two steps until $N$ news
articles were collected for annotations. To facilitate comparison, a random
sample of trustworthy news articles from the same peak periods was also
obtained. This enabled the examination of both fake and trustworthy news
articles.

Crowd-sourcing data collection. To annotate the news articles according to the
established taxonomy, a crowd-sourcing approach was employed utilizing Amazon
Mechanical Turk. The data collection process consisted of the following three
main steps. First, participants completed a short pre-study questionnaire in
which they self-reported their age and gender. Second, participants were
instructed to read the news article in detail. Third, participants were
instructed to respond to three questions:

_Q1:_ _Which company does this article mainly cover?_
(This question served to collect the company names and also served as a trap
question to assess annotation quality. Participants who failed more than one
trap question had their annotations removed from the analysis.)

_Q2:_ _Under which section of corporate coverage would you classify this
article?_
(This question collected the categories of the representative articles in
Table 4. If the assessors could not find any applicable category, they could
add their own categorization through a free-text form under the option
“Others”.)

_Q3:_ _How would you judge the company based on the article (assuming it is
true)? Please pick five words each from a different moral dimension._
(This question assessed how the company would be judged in the article, were
it to be true, along five moral dimensions: harm, fairness, loyalty,
authority, and purity (Graham et al. 2011). The participants were asked to
pick five words each from a different moral dimension. Figure 3 illustrates
the positive and negative words used for those moral dimensions for the
participants to pick from (Graham et al. 2011). We then calculated the
morality score of each moral dimension by counting the corresponding number of
positive words selected minus the number of negative words selected within
that dimension. )

A total of 103 participants were involved in this crowd-sourcing study, with
each new article being evaluated by three participants. On average, each
participant evaluated around 44 news articles. The final categorization of
each article was determined using a majority vote approach.

Figure 3: Moral dimensions along which our participants judged a company
mentioned in news articles.

###  4.3  Popular News During Misinformation Shocks

Categories of fake news articles. The top three high-level (Level 1)
categories for corporate misinformation are: _Product_ , _Politics_ and
_Societal_ issues. Among lower-level (Level 2), the top categories are
_Product–Issues–Quality_ , followed by _Politics–Lobbying_ and
_Product–Launches_ (see Table 4 for a complete breakdown). Notably, the
“Others” category accounted for only 9.7% of fake news articles, indicating
that over 90% of fake news articles were covered by the manually established
taxonomy. This partially validates the effectiveness of the taxonomy in
categorizing corporate fake news articles.

In Table 4, under the ‘Morality” column, we present the top moral dimension
with the highest absolute morality score for each fake news category. For
instance, in news articles that cover _Product-Issues-Quality,_ people tend to
perceive the discussed companies as morally wrong (i.e., negative _purity_ ,
frequently judging them as “indecent” and “obscene”.). Overall, by tracking
morality scores for all fake news articles, we found that companies that are
targeted by fake news tend to be judged as being unfair (i.e.,  _unjust_ and
_discriminatory_ in fairness), morally wrong (i.e.,  _indecent_ and _obscene_
in purity), and harmful (i.e.,  _harmful_ and _violent_ in harm). We also
compared the morality scores across different news categories. It is not
surprising that, for news articles that cover _Societal issues,_ people
perceive the mentioned companies as the most morally unacceptable, especially
in terms of being unfair and causing harm to society. In comparison, the
category of news articles _Societal Issues-Minorities-Racial Issues_ reflects
the most morally wrong companies that people are disgusted with. This result
highlights the impact of fake news on people’s moral perception of a company,
which may not align with reality.

How categories of fake news articles differ from trustworthy ones. To measure
the extent to which each category $i$ is associated with fake news as opposed
to trustworthy news, we calculated the _misinformation association $MA(i)$_
for each article category $i$: $MA(i)=\frac{P(i|fake)}{P(i|trustworthy)}$. We
found that the top 3 categories associated with fake news outlets are
_Product-Issues-Quality_ , _Politics-Lobbying_ , and _Societal Issues-
Conspiracy_ , while the top 3 associated with trustworthy news outlets are
_Brand promotion_ , _Corporate affairs-acquisition_ , and _Politics-
regulation_. This demonstrates that certain categories of news articles, such
as product-issues-quality, were more likely to be covered by _fake_ news
outlets, compared to those _trustworthy_ ones.

##  5 Which Companies Are Targeted?

To answer this question, we identified which companies tend to be targeted,
and which factors determined that targeting.

Table 5:  Differences for company metrics between companies that are targeted by fake news and those that are not. For each of the metrics, we get a set of values, each corresponding to a company. Mean, median and standard deviation, and difference between targeted and non-targeted companies with t-stats are reported. The $p$-values for the $t$-stats significance levels are: $.1(*)$, $.05(**)$, and $0.01(***)$. The statistically significant metrics are in bold. All the metrics are calculated within the time period $T_{all}$ (between 2016 and 2019). Compared to non-targeted companies, targeted ones attract more comments on Reddit, have a slightly worse external reputation, and significantly more reputable news about them shared. | targeted companies | non-targeted companies |  |
---|---|---|---|---
| mean | median | std | mean | median | std | mean diff |  $t$-stat
Glassdoor rating $(C,T_{all})$ | 3.46 | 3.43 | 0.35 | 3.39 | 3.40 | 0.33 | 0.07 | 1.49
internal employee stress score $(C,T_{all})$ | 0.01 | 0.01 | 0.00 | 0.01 | 0.01 | 0.01 | -0.00 | -0.97
stock growth $(C)$ | 0.39 | 0.51 | 0.93 | 0.51 | 0.53 | 0.56 | -0.12 | -1.16
$log$(# Reddit comments $(C,T_{all})$) | 9.40 | 9.31 | 2.10 | 5.28 | 5.52 | 2.33 | 4.12 | $14.22^{***}$
$log$(# Reddit comments per capita $(C,T_{all})$) | 1.99 | 1.99 | 2.02 | -1.04 | -0.78 | 2.11 | 3.03 | $11.25^{***}$
external reputation score $(C,T_{all})$ | 0.77 | 0.77 | 0.01 | 0.78 | 0.77 | 0.08 | -0.02 | $-2.13^{**}$
$log$(# reputable news $(C,T_{all})$) | 7.40 | 7.40 | 1.90 | 3.02 | 3.07 | 1.87 | 4.39 | 17.82***

###  5.1 Companies and Fake News Outlets

Out of the 262 S&P 500 companies discussed on Reddit during the entire period
of study $T_{all}$, 114 were targeted by fake news, and 148 were not. What
distinguishes those two types of companies? In Table 5, we show the statistics
of our metrics’ distributions across the two sets of companies. The two sets
of companies statistically differ in their public popularity (# Reddit
comments, # Reddit comments per capita and # reputable news) and their
external reputation score. In that, companies targeted by fake news are more
popular and also discussed in more negative tone.

Figure 4: Bipartite graph connecting companies (left hand side) with the fake
news outlets (right hand side) mentioning them. A company that is likely to be
target by fake news tends to be a tech company such as Facebook and Apple.
Tech companies tend to be targeted by multiple outlets, while communication
ones (e.g., Google) tend to be targeted by specific outlets such as
stonecoldtruth.com.

To investigate the specific relationship between companies and fake news
outlets targeting them, we generated a bipartite graph of company-outlet
interactions in which a fake news outlet and a company are connected to each
other if the company is targeted by the outlet. The bipartite graph for the
top 50 companies and fake news outlets are shown in Figure 4. We found that:
(1) Internet based companies, such as Facebook, Amazon, Microsoft and Apple,
attract more fake news. (2) Certain news outlets cover many companies (e.g.,
breitbart.com and dailywire.com), whereas other outlets focus on reporting a
particular sector (e.g., stonecoldtruth.com focuses on communication service
companies).

###  5.2 Factors Associated With the Targeting

For the 126 companies targeted at least once by fake news, we calculated the
probability that news discussing them are fake (pFake $(C,T_{all})$) and asked
which company characteristics influence this probability. To that end, we ran
a linear regression with pFake $(C,T_{all})$ as the dependent variable and the
company metrics in Table 3 as independent variables. We confirmed that there
is no multicollinearity among our independent variables using Variance
Inflation Factors (VIF) (Miles 2014). VIF values above 5 would suggest
multicollinearity issues, while the maximal VIF value we found was 1.44, well
below that threshold (5). Specifically, we ran three linear regression models,
with the following sets of independent variables:

  * M1: log(# Reddit comments per capita), log(# reputable news), Glassdoor rating and stock growth

  * M2: M1 \+ external reputation score

  * M3: M2 \+ internal employee stress score

The prediction results demonstrated that, despite Model M1 being the base
model, its three predictors explain around 40% of the variance in pFake
($Adj.R^{2}$=.40). Adding the external reputation score in M2 increases the
explanatory power to $Adj.R^{2}$=.42, and also adding the internal employee
stress score in M3 leads to an $Adj.R^{2}$=.43.

Taking the best linear regression model (M3), we inspected its $\beta$
coefficients (Table 6). The coefficient $p$-values are significant for company
popularity (# Reddit comments per capita), newsworthiness (# reputable news),
and external valuations (stock growth). Companies that enjoy higher popularity
among the general public (on Reddit) and reputable news publishers, yet
possess lower stock valuations, tend to be more associated with fake news.

While the coefficients for external reputation and internal employee stress
score appear insignificant, there are increasing scores from $M1$ to $M2$
(when we added external reputation), and from $M2$ to $M3$ (when we added
internal employee stress score). When considering these scores’ correlation
with the number of reputable news sources (# reputable news) and factoring in
the results from the subsequent cross-lagged analyses reported in (§6.2), it
suggests their association with fake news.

To sum up, companies that suffer severely from fake news are considered
newsworthy, garnering more attention from reputable news publishers, yet are
perceived negatively both internally and externally. Internally, employees are
more likely to comment on a stressful work environment. Externally, investors
do not highly value these companies and the general public on social media
talks less about them; and if they do mention them, they do so using negative
sentiment.

Table 6: Coefficients and statistics for M3 predicting pFake $(C,T_{all})$ from our company metrics. All metrics are calculated within the time period of $T_{all}$ (between 2016 and 2019). A company that is likely to be targeted by fake news  is the subject of more reputable news as well (i.e., it tends to be more newsworthy), and is not doing well in the stock market (lower stock growth). The significant coefficients with $p$-values at significance level above $.1$ are shown in bold. | coef | std. error | p-value
---|---|---|---
Var_name |  |  |
Intercept | -4.058 | 0.865 | 0.000
log(# Reddit comments per capita $(C,T_{all}))$ | -4.603 | 0.702 | 0.000
log(# reputable news $(C,T_{all})$) | 2.802 | 0.826 | 0.001
stock growth $(C)$ | -1.634 | 0.850 | 0.059
external reputation score $(C,T_{all})$ | -1.480 | 0.961 | 0.128
internal employee stress score $(C,T_{all})$ | 1.116 | 0.722 | 0.127
Glassdoor rating $(C,T_{all})$ | -0.683 | 0.771 | 0.378
Table 7: The $Adj.R^{2}$ and beta coefficients of the linear regression predicting $p(article\\_category|fake)$ for three categories of news articles. Companies targeted by fake news attract more comments on Reddit. However, those that are mentioned in conjunction with product and societal issues do well in the stock market as they are tech companies such as Apple, while those that are mentioned in conjunction with politics tend to do worse among their employees (in terms of their company review ratings) and typically are pharmaceutical companies such as Pfizer. Significant values are marked with * based on their significance levels $p<0.05$. Feature | _Product_ | _Politics_ | _Societal Issues_
---|---|---|---
Intercept | -1.22* | -2.14* | -1.87*
# Reddit comments per capita $(C,T_{all})$ | 2.41* | -0.73 | 1.63*
external reputation score $(C,T_{all})$ | -0.35 | 0.72 | -0.55
Glassdoor rating $(C,T_{all})$ | -0.89 | -1.24* | -0.76
internal employee stress score $(C,T_{all})$ | 0.12 | 0.24 | -0.10
stock growth $(C)$ | 1.45* | 0.54 | 1.02*
Adjusted $R^{2}$ | 0.27 | 0.20 | 0.16

###  5.3 Factors that Influence the Most Popular Categories of Fake News
Articles

To understand the determinants that impact only specific categories of fake
news articles (Table 4), we ran three linear regressions having the
probability of a fake news article belonging to each of the top 3 categories
(_Product_ , _Politics_ , and _Societal Issues_ , as defined in §4.3) as a
dependent variable, and the five company metrics of # Reddit comments per
capita, internal employee stress score, external reputation score, stock
growth, and Glassdoor rating as independent variables. Results in Table 7 show
companies that are mentioned in conjunction with the _Product_ and _Societal
Issues_ attract more comments on Reddit and do well in the stock market as
they tend to be tech companies such as Apple. This indicates when covering
high-growth companies, fake news outlets tend to cover popular and
controversial topics. However, companies linked to fake news in the realm of
_Politics_ tend to receive lower ratings from their employees. – these
typically are pharmaceutical companies such as Pfizer.

Figure 5:  Fake news shocks (peaks of $pFake(C,T_{month})$ over time, shown
with the gray bars) for a company C that: is a Tech company (Apple and
Google); is a Communication company (Facebook); has not done well in the stock
market (General Electric); attracts considerable negative mentions on social
media (JPMorgan); and attracts considerable stress mentions by its employees
(Disney).

##  6 When Companies Are Targeted?

Our next research question is to understand _when_ companies are targeted by
fake news (_RQ3_). We are particularly interested in finding out whether a
company is targeted by fake news all the time, or within those concentrated
periods, i.e., misinformation shocks (as we defined in §3.5).

Specifically, we identified misinformation peaks for company C by identifying
those months that have $pFake(C,T_{month})$ 2 standard deviations above the
mean of $pFake(C,T_{all})$ within $T_{all}$. In Figure 5, we showed several
examples of misinformation shocks plus its most aligned metric among the
three: stock valuation $(C,T_{month})$, external reputation score
$(C,T_{month})$ and internal employee stress score$(C,T_{month})$. The
companies that are shown are those for which, based on visual inspection,
misinformation shocks were aligned the most with at least one of the three
metrics. We should stress that such an alignment is a simple association and
does not speak to any causal claim though. All the metrics are normalized
using $z$-score throughout the entire periods of study (2016-2019).

###  6.1 Qualitative Case Studies

_Apple_. The company experienced two distinct instances of misinformation, or
“misinformation shocks”, in late 2016 and early 2017. The majority of the
articles during these periods focused on three main topics: the FBI’s alleged
request for Apple to add “back-doors” in their phones; false claims of a
potential recall of their phones due to alleged issues with them catching
fire; and a ransomware attempt in which hackers threatened to wipe out 300
million iPhone accounts in early 2017, however, there was no evidence that the
company was affected by this attempt. After the second misinformation shock,
the company’s stock valuation decreased by 0.2 standard deviations.

_Google_. The company experienced one instance of misinformation, or
“misinformation shock”, in late 2016. The articles during this period focused
on a single claim that the company’s ranking algorithm downgraded any news
stories that would harm the then presidential nominee of the Democratic party,
Hillary Clinton. This claim was unproven and categorically rejected by the
company. As a result of this misinformation shock, the company’s _external
reputation score_ decreased by 1.4 standard deviations.

_Facebook_. The company experienced two instances of “misinformation shocks”.
The first occurred in early 2016 and centered around the claim that the
company was not complying with European laws requiring the removal of hate
speech on its platform. One month after this shock, the company’s internal
stress score increased by 2.3 standard deviations. The second shock occurred
in early 2017 and involved two separate events related to discrimination. The
first event was an allegation that the platform had an internal document
advising on how to censor news of crimes against Black people. The company
acknowledged this document was no longer in use. The second event was an
allegation that the company’s senior engineers blocked diversity hires. The
company declined to comment on this claim but stated that they “care deeply
about diversity”.

_General Electric_. The company experienced a single instance of significant
“misinformation shock” that lasted for two months in late 2016. The majority
of the articles during this period focused on a rumor that the company
intended to sell its $3 billion Industrial Solutions business. After the
circulation of this rumor, the company’s stock valuation decreased by 0.2
standard deviations. It was later revealed that the rumor was true, as the
sale of the business was initiated in late 2017 and completed in mid-2018.

_JP Morgan_. The company experienced a single instance of significant
“misinformation shock” that lasted for two months in late 2017. The majority
of the articles during this period focused on a scandal in which the company,
JP Morgan, was alleged to have transferred $875 million into a former Nigerian
oil minister’s account, and was subsequently sued by the Nigerian government.
After this, the company’s external reputation score decreased by 2.7 standard
deviations. However, in 2022, a judge at the British High Court ruled in favor
of JP Morgan.

_Disney_. The company experienced a single instance of significant
“misinformation shock” in mid-2019. The majority of the articles during this
period focused on allegations of child pornography involving former or current
employees. In one instance, a former employee was arrested and charged with
“transferring obscene materials to a minor and attempting to entice a
minor”.111https://www.nbcnews.com/news/us-news/disney-world-employee-tried-
lure-8-year-old-girl-sexual-n1009466 This shock did not have a significant
impact on the company’s _internal stress score_ , unlike other minor instances
of misinformation that had occurred in the past, such as slow progress in
building Disney’s Pandora land in 2017 and allegations of CIA aid in the
company’s land acquisition in Florida in 2018, which did.

These are just examples of a general trend that we observed: a company is not
targeted by fake news all the time, but there are particular times in which a
critical mass of fake news emerges, and that might impact either the company’s
_external reputation_ , _internal employee stress_ or even _stock valuation_.

###  6.2 Cross-lagged Analysis

To further examine how fake news impact company’s external reputation,
internal employee stress and stock valuation, we conducted cross-lagged
analysis. The goal is to determine causal relationships by examining the
connection between two variables at different points in time, while taking
into account the influence of earlier measurements of the same variables. This
type of analysis helps eliminate the possibility that the observed
relationship between the variables is caused by a third or confounding
variable. In our study, we conducted cross-lagged analysis on all time pairs
($t_{k}$, $t_{l}$) and selected the pair and the model based on three
conditions. The selected cross-lagged model must: 1) demonstrate significantly
stronger effects compared to a constrained model (Kenny 1975), as determined
by an ANOVA test; 2) have cross-lagged effects that are statistically
significant (at least $p<0.05$); and 3) have the highest absolute mean cross-
lagged effect among all the other models. To interpret the values of cross-
lagged effect, we employ Cohen’s statistical effect size (Cohen 2013), where
an effect size smaller than 0.2 is considered weak, an effect size between 0.2
and 0.5 is considered moderate, and an effect size between 0.5 and 1.0 is
considered strong. The cross-lagged analysis results (Figure 6) indicate that:

Figure 6: Cross-lagged analysis between pFake and various variables: (a) stock
valuation (Stock), (b) external reputation (Q-score) and (c) internal employee
stress (Stress).

Stock valuation (Figure 6a): we found convincing evidence that when a
company’s stock value goes up, it tends to continue going up in the future.
This was shown by a statistical analysis that gave a result of
$\beta_{SS}=.56$ with a probability of less than 1% that it occurred by chance
($p<0.01$). Furthermore, we noticed a moderate tendency for fake news
probability (pFake) to reinforce itself ($\beta_{FF}=.35$, $p<0.01$). This
implies that when there is a high likelihood of fake news, it is likely to be
even higher in the future. In addition, we looked at the relationship between
a company’s stock value at time $t_{k}$ and the occurrence of fake news about
that company at a later point in time $t_{l}$. The findings showed that there
is a moderate, positive connection between the two (standardized
$\beta_{SF}=.31$, $p<0.01$). This suggests that when a company’s stock value
is higher at an earlier point in time, it increases the probability that the
company will be the target of fake news at a later point in time. In contrast,
we found that exposure to fake news at time $t_{k}$ had a weak and negative
effect (standardized $\beta_{FS}=-.14$, $p<0.05$) on a company’s stock value
at a later time $t_{l}$. This means that fake news has a detrimental effect
(albeit small) on a company’s valuation, potentially leading to a decrease in
its stock value.

External reputation (Q-score) (Figure 6b): moderate evidence indicated a
positive self-reinforcement of external reputation ($\beta_{QQ}=.25$,
$p<0.01$) and pFake ($\beta_{FF}=.21$, $p<0.01$), suggesting that higher
levels of external reputation and pFake were associated with subsequent
increases in these variables. Regarding the cross-lagged effects, we examined
the influence of Q-score at $t_{k}$ on fake news occurrence at $t_{l}$, where
$t_{l}$ follows $t_{k}$. Results indicated a weak positive effect
($\beta_{QF}=.15$, $p<0.05$), suggesting that higher Q-scores at an earlier
time slightly increased the likelihood of future fake news targeting.
Conversely, a weak negative effect ($\beta_{FQ}=-.11$, $p<0.05$) was observed,
indicating that fake news exposure at $t_{k}$ had a small negative impact on
subsequent external reputation.

Internal employee stress (Stress) (Figure 6c): there was weak evidence of
stress self-reinforcement ($\beta_{SS}=.10$, $p<0.10$), indicating a slight
association between higher stress levels and subsequent stress increases.
Moderate self-reinforcement of pFake was found ($\beta_{FF}=.20$, $p<0.01$),
linking higher pFake levels to increased pFake in the future. Regarding the
cross-lagged effects, stress at $t_{k}$ positively influenced the likelihood
of future fake news targeting at $t_{l}$ with a weak magnitude
($\beta_{SF}=.14$, $p<0.05$). Similarly, fake news exposure at $t_{k}$ had a
weak positive effect on subsequent stress levels at $t_{l}$($\beta_{FS}=.09$,
$p<0.10$).

To summarize, our cross-lagged analysis revealed that higher past stock
valuations increase the future risk of fake news targeting. Weaker effects
were observed for external reputation, where higher past reputation makes
companies prone to future fake news targeting. Conversely, being targeted by
past fake news leads to reputation decline. Regarding internal stress, high
past stress levels make companies susceptible to future fake news targeting,
and past targeting increases future employee stress. These findings highlight
the interplay between stock valuation, reputation, stress, and fake news,
indicating risks for targeted companies.

##  7 Discussion

Limitations. Our study has five limitations that should be acknowledged.
First, we only attribute fake news to outlets, not individual articles.
However, research has shown that publisher-level credibility scores are
similar to article-level ones (Xu 2021). Second, our analysis of company
perceptions is based on data from Reddit and Glassdoor, so it is unclear if
the conclusions hold true on other platforms. Specifically, Reddit is an
anonymous platform without the concept of ‘friends’, unlike many other social
networks. As such, this platform is missing social pressure, and users are
less likely to enter echo chambers, which means that misinformation spreading
is not as likely to stem from homophily (McPherson, Smith-Lovin, and Cook
2001) at the circle-of-friends level as it is on other social networks.
Third, we studied the sentiment of news mentioning a company and stress
mentions in company reviews written by employees. These aspects are often
overlooked. We used state-of-the-art methods to proxy these metrics, and our
results show that they are partly predictive of misinformation. However, our
study does not capture all important factors that influence public perception
of a company, and might not generalize for other industries or countries. For
example, we did not consider corporate social responsibility (Lindgreen and
Swaen 2010) or brand identity (Balmer 2008). These aspects are still subject
of ongoing research, and we leave their exploration for future work. Fourth,
our data did not include comments that were deleted before being collected, so
we could not examine if they contained news URLs. In particular, comments that
were removed by Automoderator (bots) were unavailable to us, as these comments
were removed as soon as they were posted. Nevertheless, the Reddit dataset
from pushshift.io remains one of the most comprehensive datasets available
(Baumgartner et al. 2020). Fifth, our study was conducted on a sample of S&P
500 companies in the United States. It is possible that our findings may not
generalize to other industries, countries, or time periods. Future research is
needed to further investigate the generalizability of our findings.

Implications. Our study has three main _theoretical implications_. First, we
provide a fine-grained taxonomy of fake news coverage in the corporate world.
Second, we identify the factors that contribute to a company’s vulnerability
to misinformation (e.g., a company’s external and internal perceptions).
Third, we contribute to understanding the temporal patterns of the
misinformation, and how it varies over time. Our findings offer an initial
step towards the development of a theoretical framework for understanding the
complex phenomenon of corporate misinformation.

Our study has four main _practical implications_. First, our findings help in
identifying which companies are more susceptible to misinformation such as
financial performance and reputation, allowing them to take more proactive
measures to prevent the spread of false information. Second, understanding the
categories of corporate misinformation that are prevalent, allows companies
and stakeholders to better recognize and develop tools and strategies for
combating corporate misinformation. Third, by tracking temporal patterns of
misinformation, and detecting “misinformation shocks” early on, companies can
anticipate and promptly respond to those potential misinformation crises.
Fourth and final, our results demonstrate the importance of a systematic
monitoring system for companies, as well as transparent and direct
communication with employees to manage a possible misinformation crisis better
and faster (Holtom, Edmondson, and Niu 2020).

Broader perspective, ethics and competing interests. Our research originates
from a lab within a for-profit, traded company. As such, the authors may
benefit financially from the implementation of stronger measures against
perpetrators of misinformation. We acknowledge this potential conflict of
interest and affirm that the research was conducted with integrity and that
the results presented are unbiased and fully reflect all our findings. Our
datasets were collected from publicly available sources. We also confirm that
our conclusions are based solely on the data and analysis presented, and are
not influenced by any financial gain or benefit that may result from the
implementation of these measures. We have used rigorous statistical methods to
analyze the data and drawn conclusions that are supported by evidence.
Furthermore, we will make our data publicly available to allow for
replicability.

Our study was reviewed by the internal company ethical committee while the
study protocol, data collection methods, informed consent process, and other
aspects of the study ensured that we met ethical and legal standards. The part
of the study that involved human subjects was a crowd-sourcing user study,
where participants were paid at least the minimum wage. All our analysis were
based on aggregated data without tracking down to individuals.

##  8 Conclusion

Our study delved into the complex issue of corporate misinformation,
specifically focusing on the S&P 500 companies. Utilizing a combination of
large-scale social media and crowd-sourcing data, we uncovered key insights
into the nature of fake news targeting these companies. We found that fake
news outlets often report on a company’s products, politics, and societal
issues when spreading misinformation. Furthermore, we established a clear link
between a company’s external and internal perceptions and its likelihood of
being targeted by fake news. Companies that are struggling in the stock
market, receiving negative mentions on social media, and whose employees are
facing stress are more likely to be targeted by fake news. Additionally, we
discovered that fake news about a company does not occur consistently, but
rather emerges in specific, concentrated periods. These findings have
significant implications for companies and stakeholders looking to combat the
spread of misinformation.

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##  Appendix A Appendix

Our paper has five main findings222https://social-dynamics.net/fake-news/:

  1. 1.

Companies that suffer severely from fake news are also likely to be covered by
reputable news publishers.

  2. 2.

Companies susceptible to fake news receive less attention from the general
public on social media.

  3. 3.

Being targeted by fake news leads to a higher likelihood of public comments
with negative sentiments on social media.

  4. 4.

A company targeted by fake news is likely to experience a lower stock market
growth.

  5. 5.

In companies targeted by fake news, employees are more likely to express
stress indicators in their reviews.

In recent advancements, the efficacy of Large Language Models (LLMs) in
elucidating causal relationships has emerged as a potential application
(Kıcıman et al. 2023). Building upon this work (Kıcıman et al. 2023), we
embark on an exploration of causal analysis leveraging the capabilities of
GPT4, a state-of-the-art language model333https://openai.com/blog/gpt-4-api-
general-availability. Our primary objective revolves around validating our
five findings. To achieve this, we adopt prompt strategies from (Kıcıman et
al. 2023) aimed at validating each causal claim by evaluating its normality or
abnormality. A typical prompt includes:

_System_ : You are a helpful assistant for causal reasoning. Specifically, you
are an advanced expert specializing in causal reasoning within the realm of
Corporate Misinformation research using public data, such as stock price,
published news, and social media responses. Equipped with a strong knowledge
base and experience in financial, social and environmental determinants of
misinformation in the company world, you are proficient in identifying,
assessing, and interpreting the complex interactions among variables. Your
primary responsibility involves validating known or hypothesized causal
relationships between a diverse array of variables and their respective
outcomes.

_User:_ You will receive a claim on the cause-and-effect relationship between
variables. Based on all the information, given a causal event that causes an
outcome event at a later time point, the causal event is “abnormal” if the
causal event’s occurrence was unexpected, unlikely, surprising, rare, or
improbable. The opposite of “abnormal” is “normal”. A causal event is ‘normal’
if its occurrence was expected, likely, probable, and not surprising. The
normality of a causal event is independent of whether the outcome event was
bad or and whether the outcome event was normal. Please read the following
statement about a causal event. Provide your step-by step rationale as to why
the causal event is abnormal or normal. Then, return the word “normal” if you
think the causal event is normal or “abnormal” if you think the causal event
is abnormal.

_Claim_ : _Companies that suffer severely from fake news are also likely to be
covered by reputable news publishers._

The step-by-step answer of GPT-4 to that typical prompt unfolds as follows:
“The claim suggests a causal relationship between severe suffering from fake
news by companies and increased coverage by reputable news publishers. Let’s
break this down step by step:

_Companies suffering severely from fake news_ : This implies a situation where
a company is significantly affected by false information spread about it.

_Likelihood of coverage by reputable news publishers_ : The claim implies that
when a company faces severe fake news attacks, reputable news outlets are more
likely to cover the situation.

_Rationale for Abnormality or Normality_ :

  * •

_Abnormal_ : This relationship might be considered abnormal because,
traditionally, reputable news publishers tend to verify and fact-check
information rigorously before reporting. Therefore, if a company is suffering
from fake news, the expectation might be that reputable news sources would be
cautious about covering unverified or misleading information, potentially
leading to less coverage rather than more.

  * •

_Normal_ : On the other hand, in today’s media landscape, there’s a growing
emphasis on reporting on significant events, even if they are controversial or
involve misinformation. Companies facing severe fake news might attract
attention due to the sensational nature of the situation, prompting reputable
news outlets to cover it to clarify the facts and counteract the false
information.

Considering the dynamics of sensationalism in media and the role of reputable
news sources in countering fake news, the relationship might indeed be normal.
While it might seem unexpected for reputable sources to cover such companies
more, the desire to address and rectify false information could drive
increased coverage. _Therefore, the claim is _normal_ in this scenario._

Statement |  Conclusion
---|---
Companies that suffer severely from fake news are also likely to be covered more by reputable news publishers. |  Considering the dynamics of sensationalism in media and the role of reputable news sources in countering fake news, the relationship might indeed be normal. While it might seem unexpected for reputable sources to cover such companies more, the desire to address and rectify false information could drive increased coverage. _Therefore, the claim is _normal_ in this scenario._
Companies susceptible to fake news receive less attention from the general public on social media. |  Considering the dynamics of how fake news spreads on social media and the potential hesitancy to engage with content that might be false, the relationship between companies being subject to fake news and the general public talking less about them on social media might be considered normal. The decrease in discussion could be a result of cautious behavior among the public to avoid amplifying false information. _Therefore, the claim is _normal_ in this scenario._
Being targeted by fake news leads to a higher likelihood of public comments with negative sentiments on social media, contributing to the decline in the reputation of affected companies. |  Considering the typical response patterns to fake news and its impact on reputation, the relationship between being targeted by fake news, experiencing reputation decline, and receiving negative comments on social media is more likely to be normal. The emotional response triggered by fake news and its effect on public sentiment could drive increased negative comments on social media. _Therefore, the claim is _normal_ in this scenario._
A company targeted by fake news is likely to experience a lower stock market growth. |  Considering the complexities of the stock market and the multitude of factors influencing stock prices, the relationship between being likely to be targeted by fake news and maintaining lower stock growth might be normal. While other market dynamics are at play, the impact of negative news or misinformation on stock performance could lead to lower growth. _Therefore, the claim is _normal_ in this scenario._
In companies targeted by fake news, employees are more likely to express stress indicators in their reviews. |  Considering the potential impact of negative external events on employee morale and the prevalence of stress-related discussions in workplace environments, the relationship between companies susceptible to fake news targeting and considerable stress mentions by their employees might be considered normal. The spread of false information about the company could indeed impact the internal environment and employee well-being. _Therefore, the claim is _normal_ in this scenario._
Employees express stress indicators when reviewing their workplaces are more likely to be targeted by fake news. |  Considering the link between employee stress indicators and potential targeting by fake news, it might be considered abnormal as the direct causal relationship between these variables could be unexpected or unlikely. The connection might involve other complex factors beyond just stress indicators. _Therefore, the causal event is _abnormal_ in this scenario._
Table 8: Validation of our five main findings and one unsubstantiated claim by
prompting LLM GPT-4 for causality as per (Kıcıman et al. 2023). Causal
relationships are considered validated if found to be normal.

The resulting assessments of the (ab)normality of our five claims (Table 8)
suggest that all five of our main findings are supported. However, the LLM
does not always return ’normal’ as an answer. To illustrate this, we also
considered statements for which our causal analysis did not have strong
evidence to be conclusive. One such statement is reported in the last row of
Table 8 and suggests that companies in which employees express stress
indicators when reviewing their workplaces are more likely to be targeted by
fake news. Indeed, the LLM found this claim to be abnormal.