United States

Black Lives Matter (BLM) is a decentralized social movement protesting violence against Black individuals and communities, with a focus on police brutality. The movement gained significant attention following the killings of Ahmaud Arbery, Breonna Taylor, and George Floyd in 2020. The #BlackLivesMatter social media hashtag has come to represent the grassroots movement, with similar hashtags counter protesting the BLM movement, such as #AllLivesMatter, and #BlueLivesMatter. We introduce a data set of 63.9 million tweets from 13.0 million users from over 100 countries which contain one of the following keywords: BlackLivesMatter, AllLivesMatter, and BlueLivesMatter.  This data set contains all currently available tweets from the beginning of the BLM movement in 2013 to 2021. We summarize the data set and show temporal trends in use of both the BlackLivesMatter keyword and keywords associated with counter movements.  Additionally, for each keyword, we create and release a set of Latent Dirichlet Allocation (LDA) topics (i.e., automatically clustered groups of semantically co-occuring words) to aid researchers in identifying linguistic patterns across the three keywords.

ICWSM 2022

Twitter Corpus of the #BlackLivesMatter Movement And Counter Protests: 2013 to 2021

black lives matter

twitter

social media

**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.

poster

Chile experienced a series of important protests between October and December 2019, fueled by the country's significant social inequity.This social outburst, as it was called, radically affected the nation's status quo. A large portion of the population demanded a new constitution and changes to the current government, whereas another part of the population rejected these social demands and institutional reforms. This created a highly polarized scenario that was evidenced in online social media interactions. Here we analyze group polarization in social networks by studying the 2019 Chilean social unrest movement. Specifically, we propose an unsupervised approach for identifying and characterizing community framing (i.e., discovering and understanding polarized concepts) in online social networks. Our approach is based on the sequential application of community detection, topic modeling, and word embedding methods. The novelty of having an unsupervised approach is that it facilitates the performance of scalable and objective framing analyses with minimal human intervention, as it does not require prior domain or network knowledge. Using this methodology, we observe that an apparently similar conversation topic across communities can actually have completely different meanings for each group. We noted, for instance, that while an online community linked the term gente (people) with communism and terrorism, the other associated it with police and military aggression to citizenship. Analyzing controversial issues that emerge naturally from conversations in online communities offers a deeper and great-scale understanding of today's political and societal discussions. In this direction, our findings have implications for contextualizing real-world social issues on online platforms, describing how users discuss similar concepts with opposing views. In addition, despite that communities with opposing views discussed similar concepts, our results also provide clues that conversations could converge to common themes, bridging the gap in polarized discussions.

Identifying and Characterizing new Expressions of Community Framing during Polarization

The intensification of affective polarization worldwide has raised new questions about how social media platforms might be further fracturing an already-divided public sphere.  We explore if prompting perspective-taking on social media platforms can help enhance empathy between opposing groups as a first step towards reducing affective polarization.  Specifically, we deploy a randomized field experiment through a browser extension to 1,611 participants on Twitter, which enables participants to replace their feeds with those belonging to accounts who hold different political views.  We find that simply exposing participants to "outgroup" feeds enhances engagement, but not an understanding of why others hold their political views.  On the other hand, framing the experience in familiar, empathic terms by prompting participants to recall a disagreement with a friend does not affect engagement, but does increase their ability to understand opposing views.  Our findings illustrate how social media platforms might take simple steps that align with business objectives to reduce affective polarization.

Perspective-taking to Reduce Affective Polarization on Social Media

Though significant efforts such as removing false claims and promoting reliable sources have been increased to combat COVID-19 ``misinfodemic'', it remains a societal challenge if lacking a good understanding of susceptible online users, i.e., those who are likely to be attracted by, believe and spread misinformation. This study attempts to tackle this problem by understanding {\it who} constitutes the population vulnerable to the online misinformation in the pandemic, and what are the robust features and short-term behavior signals that distinguish susceptible users from others. Using a 6-month longitudinal user panel on Twitter collected from a geopolitically diverse context in US, we distinguish different types of users, ranging from social bots to humans with various level of engagement with COVID-related misinformation. We then identify users' online features and situational predictors that correlate with their susceptibility to COVID-19 misinformation. This work brings unique contributions: First, contrary to the prior studies on bot influence, our analysis shows that social bots' contribution to misinformation sharing was surprisingly low, and human-like users' misinformation behaviors exhibit heterogeneity. While the sharing of misinformation was highly concentrated, the risk of occasionally sharing misinformation for average users remained alarmingly high. Second, our findings highlight the political sensitivity, activeness and responsiveness to emotionally-charged content among susceptible users. Third, using an interpretable deep learning model, \addr{we demonstrate a feasible solution to efficiently predict users' transient susceptibility solely based on their short-term news consumption and exposure from their networks.} Our methodology and results have an implication in designing effective intervention mechanism to mitigate the misinformation dissipation.

Characterizing User Susceptibility to COVID-19 Misinformation on Twitter

Wikipedia, a collaborative crowd-sourced platform, is the largest, most visited online encyclopedia. Since it spreads information freely in more than 300 languages, many users, tools, and dashboards rely on its content. Hence, there is a need to maintain its fairness and completeness. However, previous research has indicated the existence of a significant gender gap in Wikipedia biographical articles. We already know that a minimal proportion of those articles portray women and there are gender asymmetries in the textual content of these articles, but little has been reported about the visual aspects (e.g., image volume or image quality) of the gender gap. Here, we analyze all biographies available on Wikipedia across 300 occupations in the ten most widely spoken languages, and undertake quantitative and qualitative analysis of gender differences in the written and visual content. The cross-lingual results indicate that (1) much of the male bias in content arises when editors select which personalities should have a Wikipedia page, (2) the trends in written and visual content are quite dissimilar, (3) men biographies tend to have more images across languages, and (4) female biographies average better visual quality. A more granular analysis is performed on English Wikipedia, distinguishing trends in occupations and qualitatively analyzing science and technology biographies. The overall results shed light on the kinds of visual biases that emerge in the collaborative creation
of Wikipedia and yield guidelines for future management of contributions on the platform.

Visual Gender Biases in Wikipedia: A Systematic Evaluation across the Ten Most Spoken Languages

The governance of many online communities relies on rules created by participants. However, prior work provides limited evidence about how these self-governance efforts compare and relate to one another across communities. Studies tend either to analyze communities as discrete entities or consider communities that coexist within a hierarchically-managed platform. In this paper, we investigate both comparative and relational dimensions of self-governance in similar communities. We use exhaustive trace data from the five largest language editions of Wikipedia over almost 20 years since their founding, and consider both patterns in rule-making and overlaps in rule sets. We find similar rule-making activity across the five communities that replicates and extends prior work on English language Wikipedia alone. However, we also find that these Wikipedias have increasingly unique rule sets, even as editing activity concentrates on rules shared between them. Self-governing communities aligned in key ways may share a common core of rules and rule-making practices as they develop and sustain institutional variations.

Rules and Rule-making in the Five Largest Wikipedias

Many applications capture in digital form aspects of people’s lives. The resulting data, which we call Personal Digital Traces - PDTs, can be used to help reconstruct people’s episodic memories and connect to their past personal events. This may have several applications, from helping the recall of patients with neurodegenerative diseases to gathering clues from multiple sources to identify recent contacts and places visited – a critical new application for the current health crisis. This paper takes steps towards integrating and summarizing the heterogeneous collection of data into episodic narratives using scripts - prototypical plans for everyday activities. Specifically, we propose a matching algorithm that groups PDTs from many different sources into script instances (episodes), and we provide a technique for ranking the likelihood of candidate episodes.
We report on the results of a study based on the personal data of real users, which gives evidence that our episode reconstruction 1) integrates well PDTs from different sources into coherent episodes, and 2) augments users’ memory of their past actions.

Supporting Human Memory by Reconstructing Personal Episodic Narratives from Digital Traces

Analyzing gender is critical to study mental health (MH) support in CVD (cardiovascular disease). The existing studies on using social media for extracting MH symptoms consider symptom detection and tend to ignore user context, disease, or gender. The current study aims to design and evaluate a system to capture how MH symptoms associated with CVD are expressed differently with the gender on social media. We observe that the reliable detection of MH symptoms expressed by persons with heart disease in user posts is challenging because of the co-existence of (dis)similar MH symptoms in one post and due to variation in the description of symptoms based on gender. We collect a corpus of 150k items (posts and comments) annotated using the subreddit labels and transfer learning approaches. We propose GeM, a novel task-adaptive multi-task learning approach to identify the MH symptoms in CVD patients based on gender. Specifically, we adopt a knowledge-assisted RoBERTa based bi-encoder model to capture CVD-related MH symptoms. Moreover, it enhances the reliability for differentiating the gender language in MH symptoms when compared to the state-of-art language models. Our model achieves high (statistically significant) performance and predicts four labels of MH issues and two gender labels, which outperforms RoBERTa, improving the recall by 2.14%  on the symptom identification task and by 2.55% on the gender identification  task.

A Computational Approach to Understand Mental Health from Reddit: Knowledge-aware Multitask Learning Framework

Who actually expresses an intent to buy GameStop shares on Reddit? What convinces people to buy stocks? Are people convinced to support a coordinated plan to adversely impact Wall Street investors? Existing literature on understanding intent has mainly relied on surveys and self reporting; however there are limitations to these methodologies. Hence, in this paper, we develop an annotated dataset of communications centered on the GameStop phenomenon to analyze the subscriber intentions behaviors within the r/WallStreetBets community to buy (or not buy) stocks. Likewise, we curate a dataset to better understand how intent interacts with a user's general support towards the coordinated actions of the community for GameStop. Overall, our dataset can provide insight to social scientists on the persuasive power to buy into social movements online by adopting common language and narrative.  WARNING: This paper contains offensive language that commonly appears on Reddit's r/WallStreetBets subreddit.

Turning Stocks into Memes: A Dataset for Understanding How Social Communities Can Drive Wall Street

Databases of highly networked individuals have been indispensable in studying narratives and influence on social media. To support studies on Twitter in India, we present a systematically categorized database of accounts of influence on Twitter in India, identified and annotated through an iterative process of friends, networks, and self-described profile information, verified manually. We built an initial set of accounts based on the friend network of a seed set of accounts based on real-world renown in various fields, and then snowballed ``friends of friends" multiple times, and rank ordered individuals based on the number of in-group connections, and overall followers. We then manually classified identified accounts under the categories of entertainment, sports, business, government, institutions, journalism, civil society accounts that have independent standing outside of social media, as well as a category of ``digital first" referring to accounts that derive their primary influence from online activity. Overall, we annotated 11580 unique accounts across all categories. The database is useful studying various questions related to the role of influencers in polarization, misinformation, extreme speech, political discourse etc.

DISMISS: Database of Indian Social Media Influencers (Snowballed Sequentially) on Twitter

Social Media is equipped with the ability to track and quantify user behaviour, establishing it as an appropriate resource for mental health studies. However, previous efforts in the area have been limited by the lack of data or contextually relevant information. There is a need for large-scale, well-labelled mental health datasets with fast reproducible methods to facilitate a heuristically growing dataset. In this paper, we cater to this need by building the Twitter - Self-Reported Temporally-Contextual Mental Health Diagnosis Dataset (Twitter-STMHD), a large scale, user-level dataset grouped into 8 disorder categories and a companion class of control users. The dataset is 60% hand-annotated, which lead to the creation of high-precision self-reported diagnosis report patterns, contributing to the rest of the dataset. This dataset records user-profiles and all tweets from relevant periods like onset and progression of disorder by leveraging upon temporal data. This is the only largest dataset that tries to capture the tweeting activity of users suffering from mental health disorders during the COVID-19 period.

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