United States

Voat was a news aggregator website that shut down on December 25, 2020. The site had a troubled history and was known for hosting various banned subreddits. This paper presents a dataset with over 2.3M submissions and 16.2M comments posted from 113K users in 7.1K subverses (the equivalent of subreddit for Voat). Our dataset covers the whole lifetime of Voat, from its developing period starting on November 8, 2013, the day it was founded, April 2014, up until the day it shut down (December 25, 2020).
This work presents the largest and most complete publicly available Voat dataset, to the best of our knowledge. We also present a preliminary analysis to cover posting activity and daily user and subverse registration on the platform so that researchers interested in our dataset can know what to expect. Our data may prove helpful to false news dissemination studies as we analyze the links users share on the platform, finding that many communities rely on alternative news press, like Breitbart and GatewayPundit, for their daily discussions. Last, we perform network analysis on user interactions finding
that many users prefer not to interact with subverses outside their narrative interests, which could be helpful to researchers focusing on polarization and echo chambers.
Also, since Voat was one of the platforms many Reddit users migrated to after a ban, we are confident that our dataset will motivate and assist researchers studying deplatforming. In addition, many hateful and conspiratorial communities seem to
be very popular on Voat, which makes our work valuable for researchers focusing on toxicity, conspiracy theories, cross-platform studies of social networks, and natural language processing.

ICWSM 2022

I can&#39;t keep it up anymore. The Voat.co dataset

fringe platform analysis

deplatforming

online hate speech

misinformation

**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.


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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.

I can't keep it up anymore. The Voat.co dataset

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

COVID-19, which was first detected in late 2019 in Wuhan, China, has spread to the rest of the world and is currently deemed a global pandemic. A flux of events, triggered by a wide ranging set of factors such as virus mutations and waves of infections, imperfect medical and policy interventions, vested interest driven political posturing, all have created a continuous state of uncertainty and strife. In this verbile environment, misinformation and fake news thrive and propagate easily through the modern, efficient all-pervading media and social media tools, resulting in an infodemic running its course in conjunction with the pandemic. In this work, we present a COVID-19 related dataset – FaCov – a compilation of fact-checking articles that examine and evaluate some of the most widely circulated rumors and claims concerning the coronavirus. We have collected articles from 13 very popular fact-checking sources, along with information about the articles and the vetted verity assigned to the claims being evaluated. We also share insights into the dataset to highlight and understand the major conversations and conflicts in narratives encompassing the pandemic.

FaCov: COVID-19 Viral News and Rumors Fact-Check Articles Dataset

As online communication continues to become more prevalent, instances of cyberbullying have also become more common, particularly on social media sites. Previous research in this area has studied cyberbullying outcomes, predictors of cyberbullying victimization / perpetration, and computational detection models that rely on labeled datasets to identify the underlying patterns. However, there is a dearth of work examining the content of what is said when cyberbullying occurs and most of the available datasets include only basic labels (cyberbullying or not). This paper presents an annotated Instagram dataset with detailed labels about key cyberbullying properties, such as the content type, purpose, directionality, and co-occurrence with other phenomena, as well as demographic information about the individuals who performed the annotations. Additionally, results of an exploratory logistic regression analysis are reported to illustrate how new insights about cyberbullying and its automatic detection can be gained from this labeled dataset.

A Labeled Dataset for Investigating Cyberbullying Content Patterns in Instagram

Given the widespread dissemination of inaccurate medical advice related to the 2019 coronavirus pandemic (COVID-19), such as fake remedies, treatments and prevention suggestions, misinformation detection has emerged as an open problem of high importance and interest for the research community. Several works study health misinformation detection, yet little attention has been given to the perceived severity of misinformation posts. 
In this work, we frame health misinformation as a risk assessment task. More specifically, we study the severity of each misinformation story and how readers perceive this severity, \ie how harmful a message believed by the audience can be and what type of signals can be used to recognize, potentially malicious, fake news and detect refuted claims. To address our research questions, we introduce a new benchmark dataset, accompanied with detailed data analysis. We evaluate several traditional and state-of-the-art models, and show there is a significant gap in performance when applying traditional misinformation models to detect severe misinformation. We conclude with open challenges and future directions.

Drink bleach or do what now? Covid-HeRA: A study of risk-informed health decision making in the presence of COVID19 misinformation

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.

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

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.

I can't keep it up anymore. The Voat.co dataset

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Identifying and Characterizing new Expressions of Community Framing during Polarization

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