United States

Concerns over the spread of abusive content in social media have led online platforms to explore large scale community-level forms of moderation.
As an alternative to full bans, Reddit has implemented quarantines of subreddits, limiting access to controversial communities without explicit removal.
Previous evaluations of the effectiveness of quarantines have focused on their impact on toxicity in affected communities.
In this work, however, we focus on examining the impacts of quarantines of political subreddits on issues of political engagement in the broader Reddit community.
We present a case study of two ideologically distinct political subreddits, r/The\_Donald and r/ChapoTrapHouse, and examine the impact of quarantines on activity, visibility, and political discussion.
We find that while quarantines had a homogenizing effect on participation in political subreddits, they had limited impact on the visibility of community-internal issues and political language, due to the entrenchment of subreddit norms.

ICWSM 2022

A Tale of Two Subreddits: Measuring the Impacts of Quarantines on Political Engagement on Reddit

political discussion

quarantines

content moderation

reddit

**ICWSM-2022 will be held from June 6th – 9th in a hybrid format, both in-person in Atlanta, Georgia and online.**

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The International AAAI Conference on Web and Social Media (ICWSM) is a forum for researchers from multiple disciplines to come together to share knowledge, discuss ideas, exchange information, and learn about cutting-edge research in diverse fields with the common theme of online social media. This overall theme includes research in new perspectives in social theories, as well as computational algorithms for analyzing social media. ICWSM is a singularly fitting venue for research that blends social science and computational approaches to answer important and challenging questions about human social behavior through social media while advancing computational tools for vast and unstructured data.


ICWSM, now in its sixteenth year, has become one of the premier venues for computational social science, and previous years of ICWSM have featured papers, posters, and demos that draw upon network science, machine learning, computational linguistics, sociology, communication, and political science. The uniqueness of the venue and the quality of submissions have contributed to a rapidly growing conference, and a competitive acceptance rate of approximately 20% for full-length research papers published in the proceedings by the Association for the Advancement of Artificial Intelligence (AAAI).


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The International AAAI Conference on Web and Social Media (ICWSM) is a forum for researchers from multiple disciplines to come together to share knowledge, discuss ideas, exchange information, and learn about cutting-edge research in diverse fields with the common theme of online social media. This overall theme includes research in new perspectives in social theories, as well as computational algorithms for analyzing social media. ICWSM is a singularly fitting venue for research that blends social science and computational approaches to answer important and challenging questions about human social behavior through social media while advancing computational tools for vast and unstructured data.

technical paper

Because  traditional social media platforms continue to ban actors spreading hate speech or other forms of abusive languages (a process known as deplatforming), these actors migrate to alternative platforms that do not moderate user content to the same degree. One popular platform relevant for the German community is Telegram for which limited research efforts have been made so far.

This study aimed to develop a broad framework comprising (i) an abusive language classification model for German Telegram messages and (ii) a classification model for the hatefulness of Telegram channels. For the first part, we use existing abusive language datasets containing posts from other platforms to develop our classification models. For the channel classification model, we develop a method that combines channel-specific content information collected from a topic model with a social graph to predict the hatefulness of channels. Furthermore, we complement these two approaches for hate speech detection with insightful results on the evolution of German speaking communities focused on hateful content on the Telegram platform. We also propose methods for conducting scalable network analyses for social media platforms to the hate speech research community. As an additional output of this study, we provide an annotated abusive language dataset containing 1,149 annotated Telegram messages.

Introducing an Abusive Language Classification Framework for Telegram to Investigate the German Hater Community

Modelling differential stress expressions in urban and rural regions in China can provide a better understanding of the effects of urbanization on psychological well-being in a country that has rapidly grown economically in the last two decades. Using Weibo posts from over 65,000 users across 329 counties, we build hierarchical mixed-effects models to understand linguistic differences in the experiences and expressions of stress in urban-rural China. We analyze phrases, topical themes, and psycho-linguistic word choices in Weibo posts mentioning stress to better understand appraisal differences surrounding psychological stress in urban and rural communities in China; we then compare them with large-scale polls from Gallup. After controlling for socioeconomic and gender differences, we find that rural communities tend to express stress in emotional and personal themes such as relationships, health, and opportunity while users in urban areas express stress using relative, temporal, and external themes such as work, politics, and economics. We find corroborative trends in physical, financial, and social wellness with urbanization in Gallup polls.

Understanding Urban-Rural Differences in Stress in China Using Hierarchical Modeling

A breadth of literature has examined how gig workers use online forums. The past literature focuses primarily on how gig workers for mainstream corporate platforms leverage forums. Yet, marginalization, stigma, censorship, and criminalization all shape how people, including gig workers, use digital technology. In this work, we seek to take a first step toward understanding how marginalized, stigmatized, digitally censored, and in some cases criminalized gig workers leverage the affordances of online public forums to build community and increase their welfare. To do so, we conduct a qualitative analysis of 4,000 posts and 25,851 comments shared over four months in a large online public forum used by sex workers for peer support. Sex workers sit at the intersection of multiple marginalized communities, and thus offer a lens into the broader use of forums by marginalized labor communities. Our findings offer insight into how these workers use moderation to preserve their safety and community. Further, we highlight similarities and differences between how this community utilizes the forum platform and prior scholarship on the use of online forums by gig workers.

Community Under Surveillance: Impacts of Marginalization on an Online Labor Forum

Evacuations have a significant impact on saving human lives during hurricanes. However, as a complex dynamic process, it is typically difficult to know  individual evacuation decisions in real-time. Since a large amount of information is continuously posted through social media platforms, we can use them to understand individual evacuation behavior. In this paper, we collect tweets during Hurricane Irma in 2017 and train a text classifier in an active learning way to distinguish tweets expressing positive evacuation decisions from both negative and irrelevant ones. Additionally, we perform a demographic analysis and content clustering to investigate the potential causes and correlates of evacuation decisions. The results can be used to help inform planning strategies of emergency response agencies.

Identifying Hurricane Evacuation Intent on Twitter

Information on social media spreads through an underlying diffusion network that connects people of common interests and opinions. This diffusion network often comprises multiple layers, each capturing the spreading dynamics of a certain type of information characterized by, for example, topic, language, or attitude. Researchers have previously proposed methods to infer these underlying multilayer diffusion networks from observed spreading patterns but little is known about how well these methods perform across the range of realistic spreading data. In this paper, we conduct an extensive series of synthetic data experiments to systematically analyze the performance of the multilayer diffusion network inference framework, under varied network structure (e.g. density, number of layers) and information diffusion settings (e.g. cascade size, layer mixing) that are designed to mimic real-world spreading on social media. Our results show extreme performance variation of the inference framework: notably, it achieves much higher accuracy when inferring a denser diffusion network, while it fails to decompose the diffusion network correctly when most cascades in the data reach a limited audience. In demonstrating the conditions under which the inference accuracy is extremely low, our paper highlights the need to carefully evaluate the applicability of the inference before running it on real data. Practically, our results serve as a reference for this evaluation, and our publicly available implementation, which outperforms previous implementations in accuracy, supports further testing under personalized settings.

Limits of Multilayer Diffusion Network Inference in Social Media Research

The Internet Engineering Task Force (IETF) is responsible for producing the standards that underpin the web (e.g., HTTP, WebSockets, and WebRTC). While the IETF follows an open, consensus-driven process, protocol standardisation is inherently social and political, and latent influential structures might exist in the community. Exploring and understanding these is essential to ensuring the IETF's resilience and openness. We use network analysis to explore the social graphs of IETF participants and the influence that key contributors have.
We show that a small core dominates: the top 10% of participants contribute 43.75% of emails and come from a relatively small set of organisations. On the other hand, we also find that influence has become relatively more decentralised with time.
IETF participants also propose and work on protocol drafts that are either adopted by a working group for further refinement or get rejected at the early stage.
Using the social graph features combined with email text features, we perform regression analysis to understand the effect of user influence on the success of proposed protocol drafts being adopted. Our findings shed useful insights into behavior of participants across time, correlation between influence and success in draft adoption, and the significance of affiliated organisations in development of protocol drafts.

The Web We Weave: Untangling the Social Graph of the IETF

Due to the rise in toxic speech on social media and other
online platforms, there is a growing need for systems that
could automatically flag or filter such content. Various supervised
machine learning approaches have been proposed,
trained from manually-annotated toxic speech corpora. However,
annotators sometimes struggle to judge or to agree on
which text is toxic and which group is being targeted in a
given text. This could be due to bias, subjectivity, or unfamiliarity
with used terminology, (e.g. domain language, slang).
In this paper, we propose the use of a knowledge graph to help
in better understanding such toxic speech annotation issues.
Our empirical results show that 3% in a sample of 19k texts
mention terms associated with frequently attacked gender and
sexual orientation groups that were not correctly identified by
the annotators.

Supporting Online Toxicity Detection with Knowledge Graphs

Emojis come with prepacked semantics making them great candidates to create new forms of more accessible communications. Yet, little is known about how much of this emojis semantic is agreed upon by humans, outside of textual contexts. Thus, we collected a crowdsourced dataset of one-word emoji descriptions for 1,289 emojis presented to participants with no surrounding text. The emojis and their interpretations were then examined for ambiguity. We find that with 30 annotations per emoji, 16 emojis (1.2%) are completely unambiguous, whereas 55 emojis (4.3%) are so ambiguous that their descriptions are indistinguishable from randomly chosen descriptions. Most of studied emojis are spread out between the two extremes. Furthermore, investigating the ambiguity of different types of emojis, we find that an important factor is the extent to which an emoji has an embedded symbolical meaning drawn from an established code-book of symbols. We conclude by discussing design implications.

On the Context-Free Amiguity of Emoji: A Data-Driven Study of 1,289 Emojis

We argue that we need to evaluate model interpretability methods {\em in the wild}, in situations where professionals make critical decisions, and models can potentially assist them. We present an {\bf in-the-wild evaluation} of token attribution based on Deep Taylor Decomposition \cite{chefer2021transformer}, {\bf with professional journalists} performing reliability assessments. We find that using this method in conjunction with RoBERTa-Large, fine-tuned on the Gossip Corpus, led to {\bf faster and better human decision-making}, as well as a more critical attitude toward news sources among the journalists. We present a comparison of human and model rationales, as well as a qualitative analysis of the journalists' experiences with machine-in-the-loop decision making.

Evaluating Deep Taylor Decomposition for Reliability Assessment in the Wild

Despite the increasing interest in cyberbullying detection, existing efforts have largely been limited to experiments on a single platform and their generalisability across different social media platforms has received less attention. We propose XP-CB, a novel cross-platform framework based on Transformers and adversarial learning. XP-CB can enhance a Transformer leveraging unlabelled data from the source and target platforms to come up with a common representation while preventing platform-specific training. To validate our proposed framework, we experiment on cyberbullying datasets from three different platforms through six cross-platform configurations, showing its effectiveness with both BERT and RoBERTa as the underlying Transformer models.

A Tale of Two Subreddits: Measuring the Impacts of Quarantines on Political Engagement on Reddit

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