United States

Platforms often host multiple online groups with overlapping topics and members. How can researchers and designers understand how related groups affect each other? Inspired by population ecology,  prior  research  in  social  computing  and human-computer  interaction  has  studied  related  groups  by correlating group size with degrees of overlap in content and membership, but has produced puzzling results: overlap is associated with competition in some contexts but with mutual-ism in others. We suggest that this inconsistency results from aggregating intergroup relationships into an overall environmental effect that obscures the diversity of competition and mutualism among related groups. Drawing on the framework of community ecology, we introduce a time-series method for inferring competition and mutualism. We then use this frame-work to inform a large-scale analysis of clusters of subreddits that all have high user overlap. We find that mutualism is more common than competition.

ICWSM 2022

Identifying Competition and Mutualism Between Online Groups

organizational ecology; time series; online communities

**ICWSM-2022 will be held from June 6th – 9th in a hybrid format, both in-person in Atlanta, Georgia and online.**

**The Workshop and Tutorial Sessions are accessible to those that registered separately for these sessions. If you registered to attend but are not able to access the these sessions from the [Schedule tab](https://underline.io/events/291/schedule?day=2022-06-06T04%3A00%3A00.000Z), please email icwsm@underline.io
**

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

Prevalent on modern social media, both hashtags and emojis are text elements that function beyond plain text. While hashtags utilize free-formed strings and are highlighted by the platform, emojis are bonded by Unicode Standard and rendered by the platforms. Yet both have been used to mark discussion topics, express sentiment, show identity, and highlight keywords. This paper takes the initiative to analyze and highlight the strong association between hashtags and emojis, not only in their usage frequency, but also in their semantics. We show that the association is strong enough for improving downstream tasks. To this end, we design a representation learning model that can learn emoji-based representations to improve hashtag prediction.

#Emoji: A Study on the Association between Emojis and Hashtags on Twitter

During the COVID-19 pandemic, local news organizations have played an integral role in keeping communities informed about the spread and impact of the virus. We explore how political, social media, and economic factors impacted the way local media have reported on COVID-19 developments at a national scale. We construct and make available a dataset of over 10,000 local news organizations across the U.S. and their social media handles. We use social media data to model the population reach of outlets, and the underlying content relationships between them. Building on this data, we analyze how local and national media covered four key COVID-19 news topics: Statistics and Case Counts, Vaccines and Testing, Public Health Guidelines, and Economic Effects. Our results show that news outlets with higher population reach have reported proportionally more on COVID-19 than more local outlets. When broken out by topic, we observe that the Statistics and Case Counts topic is covered proportionally more by outlets that have a lower population reach and are in more Republican-leaning areas, an effect which is re- versed for the Economic Effects topic. Our analysis further shows that while social media audiences generally engage less with outlets with a smaller population reach, they tend to engage more with these outlets on posts about COVID-19. People also use relatively more emotive reactions on the posts of low population reach outlets. Finally, we demonstrate that COVID-19 posts in Republican-leaning counties generally receive more comments and fewer likes than in Democratic counties.

Understanding Local News Social Coverage and Engagement at Scale During the COVID-19 Pandemic

Users often post on content-sharing platforms in the hope of attracting high engagement from viewers. Some posts receive unusual attention and go "viral", eliciting a significant response (likes, views, shares) to the creator in the form of popularity shocks. Past theories have suggested a sense of reputation as one of the key drivers of online activity and the tendency of users to repeat fruitful behaviors. Based on these, we theorize popularity shocks to be linked with changes in the behavior of users. In this paper, we propose a framework to study the changes in user activity in terms of frequency of posting and content posted around popularity shocks. Further, given the sudden nature of their occurrence, we look into the survival durations of effects associated with these shocks. We observe that popularity shocks lead to an increase in the posting frequency of users, and users alter their content to match with the one which resulted in the shock. Also, it is found that shocks are tough to maintain, with effects fading within a few days for most users. High response from viewers and diversification of content posted is found to be linked with longer survival durations of the shock effects. We believe our work fills the gap related to observing users' online behavior exposed to sudden popularity and has widespread implications for platforms, users, and brands involved in marketing on such platforms.

Effect of Popularity Shocks on User Behaviour

The outbreak of COVID‐19 had a huge global impact, and non-scientific beliefs and political polarization have significantly influenced the population's behavior. In this context, COVID vaccines were made available in an unprecedented time, but a high level of hesitance has been observed that can undermine community immunization. Traditionally, anti-vaccination attitudes are more related to conspiratorial thinking rather than political bias. In Brazil, a country with an exemplar tradition in large-scale vaccination programs, all COVID-related topics have also been discussed under a strong political bias. In this paper, we use a multi-dimensional analysis framework to understand if anti/pro-vaccination stances expressed by Brazilians in social media are influenced by political polarization. The analysis framework incorporates: techniques to automatically infer from users their political orientation, topic modeling to discover their concerns, network analysis to characterize their social behavior, and the characterization of information sources and external influence. Our main findings confirm that anti/pro stances are biased by political polarization, right and left, respectively. While a significant proportion of pro-vaxxers display haste for an immunization program and criticize the government's actions, the anti-vaxxers distrust a vaccine developed in a record time. Anti-vaccination stance is also related to prejudice against China (anti-sinovaxxers), revealing conspiratorial theories related to communism. All groups display an ``echo chamber" behavior, revealing they are not open to distinct views.

Analysis of the influence of political polarization in the vaccination stance: the Brazilian COVID-19 scenario

While billions of COVID-19 vaccines have been administered, too many people remain hesitant. Twitter, with its substantial reach and daily exposure, is an excellent resource for examining how people frame their vaccine hesitancy and to uncover vaccine hesitancy profiles. In this paper we expose our processing journey from identifying Vaccine Hesitancy Framings in a collection of 9,133,471 original tweets discussing the COVID-19 vaccines, establishing their ontological commitments, annotating the Moral Foundations they imply to the automatic recognition of the stance of the tweet authors toward any of the CoVaxFrames that we have identified. When we found that 805,336 Twitter users had a stance towards some CoVaxFrames in either the 9,133,471 original tweets or their 17,346,664 retweets, we were able to derive nine different Vaccine Hesitancy Profiles of these users and to interpret these profiles based on the ontological commitments of the frames they evoked in their tweets and on value of their stance towards the evoked frames.

From Hesitancy Framings to Vaccine Hesitancy Profiles: A Journey of Stance, Ontological Commitments and Moral Foundations

Italy was the first European country to be hit by COVID-19 in the early 2020, since then losing over 100,000 people to the disease. By the end of the vaccination campaign of 2021, 81% of the public received at least one dose. These dramatic developments were accompanied by a rigorous discussion around vaccination, both about its urgency and its possible negative effects. Twitter is one of the most popular social media platforms in the country, but pre-pandemic vaccination debate has been shown to be polarized and siloed into echo chambers. It is thus imperative to understand the nature of this discourse, with a specific focus on the vaccination hesitant individuals, whose healthcare decisions may affect their communities and the country at large. In this study we ask, how has the Italian discussion around vaccination changed during the COVID-19 pandemic, and have the unprecedented events of 2020-2021 been able to break the echo chamber around this topic? We use a Twitter dataset spanning September 2019 - November 2021 to examine the state of polarization around vaccination. We propose a hierarchical clustering approach to find the largest communities in the endorsement networks of different time periods, and manually illustrate that it produces communities of users sharing a stance. Examining the structure of these networks, as well as textual content of their interactions, we find the stark division between supporters and hesitant individuals to continue throughout the vaccination campaign. However, we find an increasing commonality in the topical focus of the vaccine supporters and vaccine hesitant, pointing to a possible common set of facts the two sides may agree on. Still, we discover a series of concerns voiced by the hesitant community, ranging from unfounded conspiracies (microchips in vaccines) to public health policy discussion (vaccine passport limitations). We recommend an ongoing surveillance of this debate, especially to uncover concerns around vaccination before the public health decisions and official messaging are made public.

Echoes through Time: Evolution of the Italian COVID-19 Vaccination Debate

Today, administering COVID-19 vaccines at a societal scale has been deemed as the most appropriate way to defend against the COVID-19 pandemic. This global vaccination drive naturally fueled a possibility of pro-vax and anti-vax users strongly expressing their supports and concerns regarding the vaccines in online social media platforms. Understanding this online discourse is crucial for policy makers. This understanding is likely to impact the success of vaccination drives and might even impact the final outcome of our fight against the pandemic. The goal of this work is to improve this understanding using the lens of Twitter-discourse data. We first develop a classifier to categorize users according to their vaccine-related stance with high precision (97%). Using this method we detect and investigate specific user-groups who posted about vaccines in pre-COVID and COVID times. We specifically identify distinct topics that these users talk about, and investigate how vaccine-related discourse has changed between pre-COVID times and COVID times. Finally, for the first time, we investigate the change of vaccine-related stances in Twitter users and shed light on potential reasons for such changes in stance.

Winds of Change: Impact of COVID-19 on Vaccine-related Opinions of Twitter users

Online harassment in the form of hate speech has been on the rise in recent years. Addressing the issue requires a combination of content moderation by people, aided by automatic detection methods. As content moderation is itself harmful to the people doing it, we desire to reduce the burden by improving the automatic detection of hate speech.  Hate speech presents a challenge as it is directed at different target groups using a completely different vocabulary.  Further the authors of the hate speech are incentivized to disguise their behavior to avoid being removed from a platform.  This makes it difficult to develop a comprehensive data set for training and evaluating hate speech detection models because the examples that represent one hate speech domain do not typically represent others, even within the same language or culture.  We propose an unsupervised domain adaptation approach to augment labeled data for hate speech detection. We evaluate the approach with three different models (character CNNs, BiLSTMs and BERT) on three different collections.  We show our approach improves Area under the Precision/Recall curve by as much as 42% and recall by as much as 278%, with no loss (and in some cases a significant gain) in precision.

Domain Adaptive Hate Speech Detection

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.

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

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.

Identifying Competition and Mutualism Between Online Groups

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