44 research outputs found
Stranger Danger! Cross-Community Interactions with Fringe Users Increase the Growth of Fringe Communities on Reddit
Fringe communities promoting conspiracy theories and extremist ideologies
have thrived on mainstream platforms, raising questions about the mechanisms
driving their growth. Here, we hypothesize and study a possible mechanism: new
members may be recruited through fringe-interactions: the exchange of comments
between members and non-members of fringe communities. We apply text-based
causal inference techniques to study the impact of fringe-interactions on the
growth of three prominent fringe communities on Reddit: r/Incel,
r/GenderCritical, and r/The_Donald. Our results indicate that
fringe-interactions attract new members to fringe communities. Users who
receive these interactions are up to 4.2 percentage points (pp) more likely to
join fringe communities than similar, matched users who do not.
This effect is influenced by 1) the characteristics of communities where the
interaction happens (e.g., left vs. right-leaning communities) and 2) the
language used in the interactions. Interactions using toxic language have a 5pp
higher chance of attracting newcomers to fringe communities than non-toxic
interactions. We find no effect when repeating this analysis by replacing
fringe (r/Incel, r/GenderCritical, and r/The_Donald) with non-fringe
communities (r/climatechange, r/NBA, r/leagueoflegends), suggesting this growth
mechanism is specific to fringe communities. Overall, our findings suggest that
curtailing fringe-interactions may reduce the growth of fringe communities on
mainstream platforms.Comment: 11 Pages, 7 Figures, 3 Table
The Amplification Paradox in Recommender Systems
Automated audits of recommender systems found that blindly following
recommendations leads users to increasingly partisan, conspiratorial, or false
content. At the same time, studies using real user traces suggest that
recommender systems are not the primary driver of attention toward extreme
content; on the contrary, such content is mostly reached through other means,
e.g., other websites. In this paper, we explain the following apparent paradox:
if the recommendation algorithm favors extreme content, why is it not driving
its consumption? With a simple agent-based model where users attribute
different utilities to items in the recommender system, we show through
simulations that the collaborative-filtering nature of recommender systems and
the nicheness of extreme content can resolve the apparent paradox: although
blindly following recommendations would indeed lead users to niche content,
users rarely consume niche content when given the option because it is of low
utility to them, which can lead the recommender system to deamplify such
content. Our results call for a nuanced interpretation of ``algorithmic
amplification'' and highlight the importance of modeling the utility of content
to users when auditing recommender systems. Code available:
https://github.com/epfl-dlab/amplification_paradox.Comment: Accepted at ICWSM'23 please cite accordingl
Automated Content Moderation Increases Adherence to Community Guidelines
Online social media platforms use automated moderation systems to remove or
reduce the visibility of rule-breaking content. While previous work has
documented the importance of manual content moderation, the effects of
automated content moderation remain largely unknown. Here, in a large study of
Facebook comments (n=412M), we used a fuzzy regression discontinuity design to
measure the impact of automated content moderation on subsequent rule-breaking
behavior (number of comments hidden/deleted) and engagement (number of
additional comments posted). We found that comment deletion decreased
subsequent rule-breaking behavior in shorter threads (20 or fewer comments),
even among other participants, suggesting that the intervention prevented
conversations from derailing. Further, the effect of deletion on the affected
user's subsequent rule-breaking behavior was longer-lived than its effect on
reducing commenting in general, suggesting that users were deterred from
rule-breaking but not from commenting. In contrast, hiding (rather than
deleting) content had small and statistically insignificant effects. Our
results suggest that automated content moderation increases adherence to
community guidelines.Comment: Accepted at TheWebConf 2023, please cite accordingl
Complexity-Aware Assignment of Latent Values in Discriminative Models for Accurate Gesture Recognition
Many of the state-of-the-art algorithms for gesture recognition are based on
Conditional Random Fields (CRFs). Successful approaches, such as the
Latent-Dynamic CRFs, extend the CRF by incorporating latent variables, whose
values are mapped to the values of the labels. In this paper we propose a novel
methodology to set the latent values according to the gesture complexity. We
use an heuristic that iterates through the samples associated with each label
value, stimating their complexity. We then use it to assign the latent values
to the label values. We evaluate our method on the task of recognizing human
gestures from video streams. The experiments were performed in binary datasets,
generated by grouping different labels. Our results demonstrate that our
approach outperforms the arbitrary one in many cases, increasing the accuracy
by up to 10%.Comment: Conference paper published at 2016 29th SIBGRAPI, Conference on
Graphics, Patterns and Images (SIBGRAPI). 8 pages, 7 figure
Quotatives Indicate Decline in Objectivity in U.S. Political News
According to journalistic standards, direct quotes should be attributed to
sources with objective quotatives such as "said" and "told", as nonobjective
quotatives, like "argued" and "insisted" would influence the readers'
perception of the quote and the quoted person. In this paper, we analyze the
adherence to this journalistic norm to study trends in objectivity in political
news across U.S. outlets of different ideological leanings. We ask: 1) How has
the usage of nonobjective quotatives evolved? and 2) How do news outlets use
nonobjective quotatives when covering politicians of different parties? To
answer these questions, we developed a dependency-parsing-based method to
extract quotatives and applied it to Quotebank, a web-scale corpus of
attributed quotes, obtaining nearly 7 million quotes, each enriched with the
quoted speaker's political party and the ideological leaning of the outlet that
published the quote. We find that while partisan outlets are the ones that most
often use nonobjective quotatives, between 2013 and 2020, the outlets that
increased their usage of nonobjective quotatives the most were "moderate"
centrist news outlets (around 0.6 percentage points, or 20% in relative
percentage over 7 years). Further, we find that outlets use nonobjective
quotatives more often when quoting politicians of the opposing ideology (e.g.,
left-leaning outlets quoting Republicans), and that this "quotative bias" is
rising at a swift pace, increasing up to 0.5 percentage points, or 25% in
relative percentage, per year. These findings suggest an overall decline in
journalistic objectivity in U.S. political news.Comment: Repo: https://github.com/epfl-dlab/quotative_bia
Understanding Online Migration Decisions Following the Banning of Radical Communities
The proliferation of radical online communities and their violent offshoots
has sparked great societal concern. However, the current practice of banning
such communities from mainstream platforms has unintended consequences: (I) the
further radicalization of their members in fringe platforms where they migrate;
and (ii) the spillover of harmful content from fringe back onto mainstream
platforms. Here, in a large observational study on two banned subreddits,
r/The\_Donald and r/fatpeoplehate, we examine how factors associated with the
RECRO radicalization framework relate to users' migration decisions.
Specifically, we quantify how these factors affect users' decisions to post on
fringe platforms and, for those who do, whether they continue posting on the
mainstream platform. Our results show that individual-level factors, those
relating to the behavior of users, are associated with the decision to post on
the fringe platform. Whereas social-level factors, users' connection with the
radical community, only affect the propensity to be coactive on both platforms.
Overall, our findings pave the way for evidence-based moderation policies, as
the decisions to migrate and remain coactive amplify unintended consequences of
community bans.Comment: 19 pages, 3 figures, 3 table
Spillover of Antisocial Behavior from Fringe Platforms: The Unintended Consequences of Community Banning
Online platforms face pressure to keep their communities civil and
respectful. Thus, the bannings of problematic online communities from
mainstream platforms like Reddit and Facebook are often met with enthusiastic
public reactions. However, this policy can lead users to migrate to alternative
fringe platforms with lower moderation standards and where antisocial behaviors
like trolling and harassment are widely accepted. As users of these communities
often remain \ca across mainstream and fringe platforms, antisocial behaviors
may spill over onto the mainstream platform. We study this possible spillover
by analyzing around users from three banned communities that migrated
to fringe platforms: r/The\_Donald, r/GenderCritical, and r/Incels. Using a
difference-in-differences design, we contrast \ca users with matched
counterparts to estimate the causal effect of fringe platform participation on
users' antisocial behavior on Reddit. Our results show that participating in
the fringe communities increases users' toxicity on Reddit (as measured by
Perspective API) and involvement with subreddits similar to the banned
community -- which often also breach platform norms. The effect intensifies
with time and exposure to the fringe platform. In short, we find evidence for a
spillover of antisocial behavior from fringe platforms onto Reddit via
co-participation.Comment: 18 pages, 4 figures, 2 tables, submitte
Protection from Evil and Good: The Differential Effects of Page Protection on Wikipedia Article Quality
Wikipedia, the Web's largest encyclopedia, frequently faces content disputes
or malicious users seeking to subvert its integrity. Administrators can
mitigate such disruptions by enforcing "page protection" that selectively
limits contributions to specific articles to help prevent the degradation of
content. However, this practice contradicts one of Wikipedia's fundamental
principlesthat it is open to all contributorsand may hinder further
improvement of the encyclopedia. In this paper, we examine the effect of page
protection on article quality to better understand whether and when page
protections are warranted. Using decade-long data on page protections from the
English Wikipedia, we conduct a quasi-experimental study analyzing pages that
received "requests for page protection"written appeals submitted by
Wikipedia editors to administrators to impose page protections. We match pages
that indeed received page protection with similar pages that did not and
quantify the causal effect of the interventions on a well-established measure
of article quality. Our findings indicate that the effect of page protection on
article quality depends on the characteristics of the page prior to the
intervention: high-quality articles are affected positively as opposed to
low-quality articles that are impacted negatively. Subsequent analysis suggests
that high-quality articles degrade when left unprotected, whereas low-quality
articles improve. Overall, with our study, we outline page protections on
Wikipedia and inform best practices on whether and when to protect an article.Comment: Under Review, 11 page