United States

New social networks and platforms such as Telegram, Gab and Parler offer a stage for extremist, racist and aggressive content, but also provide a safe space for freedom fighters in authoritarian regimes. Data from such platforms offer excellent opportunities for research on issues such as linguistic bias and toxic language detection. However, only a few, mostly unannotated, English-only corpora from such platforms exist. This article presents a new Telegram corpus in Russian and Belorussian languages tailored for research on linguistic bias in political news. In addition, we created a repository to make all currently available corpora from so-called &quot;dark&quot; platforms accessible in one place.

ICWSM 2022

BelElect: a New Dataset for Bias Research from a &quot;Dark&quot; Platform

dark' platforms

linguistic bias

telegram

### Welcome to the Brazilian Symposium on Multimedia Systems and Web 
Promoted annually by the Brazilian Computer Society (SBC), the Brazilian Symposium on Multimedia Systems and Web (WebMedia) is the main event on the subject in Brazil and an excellent opportunity for scientific and technical exchanges between students, researchers and professionals in the areas of Multimedia, Hypermedia and Web. This year, we will be celebrating the 27th edition of WebMedia, to be held from **November 5th to 12th, 2021**, organized by the Federal University of Minas Gerais and Federal University of São João Del Rei. For the second time in its history, the event will be held online, with interaction between the participants being done through tools and web platforms.
 <iframe width="560" height="315" src="https://www.youtube.com/embed/2GvWKRUDtAY" title="YouTube video player" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture" allowfullscreen></iframe> 
The Webmedia 2021 schedule will feature:
 
* Technical sessions for the presentation of scientific articles.
* National and international lectures
* Panel with Companies
* Workshops
* Short courses
* Project meetings open to the community
 

The topics of interest for paper submission are:
 
* Web and Social Networks
* Multimedia
* Digital TV, Ubiquitous and Mobile Computing
 

In addition to the main track, WebMedia 2021 will feature the Tools and Applications Workshop, the Scientific Initiation Papers Contest, the Theses and Dissertations Contest, the RNP CT-Video Workshop.
 
The organization of WebMedia 2021 is under the responsibility of the [Department of Computer Science at UFMG (DCC/UFMG)](http://dcc.ufmg.br/) and [Department of Computer Science at UFSJ (DCOMP/UFSJ)](https://dcomp.ufsj.edu.br/)
 
**#PartiuWebMedia2021 #WebMedia2021**

WebMedia 2021

The Brazilian Symposium on Multimedia Systems and Web (WebMedia) is the main event on the subject in Brazil and an excellent opportunity for scientific and technical exchanges between students, researchers and professionals in the areas of Multimedia, Hypermedia and Web.

New social networks and platforms such as Telegram, Gab and Parler offer a stage for extremist, racist and aggressive content, but also provide a safe space for freedom fighters in authoritarian regimes. Data from such platforms offer excellent opportunities for research on issues such as linguistic bias and toxic language detection. However, only a few, mostly unannotated, English-only corpora from such platforms exist. This article presents a new Telegram corpus in Russian and Belorussian languages tailored for research on linguistic bias in political news. In addition, we created a repository to make all currently available corpora from so-called "dark" platforms accessible in one place.

BelElect: a New Dataset for Bias Research from a "Dark" Platform

poster

**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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A ticket is required to attend the event, please use the below URL to register for the event if you have not already done so:

https://aaaiconf.cventevents.com/event/be8c26b0-a76f-4753-babb-de2bd9d2a619/summary

Virtual-Only:
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Registration Is Required

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.

In this paper, we present a dataset of over 1.4M online news articles from 313 local U.S. news outlets published over 20 months (between April 4th, 2020 and December 31st, 2021). These outlets cover a geographically diverse set of communities across the United States. In order to estimate characteristics of the local audience, included with this news article data is a wide range of county-level metadata, including demographics, 2020 Presidential Election vote shares, and community resilience estimates from the U.S. Census Bureau. The NELA-Local dataset can be found at: https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/GFE66K.

NELA-Local: A Dataset of U.S. Local News Articles for the Study of County-level News Ecosystems

In this paper we present a near-complete dataset of over 3M videos from 61K channels over 2.5 years (June 2019 to December 2021) from the social video hosting platform BitChute, a commonly used alternative to YouTube. Additionally, we include a variety of video-level metadata, including comments, channel descriptions, and views for each video. The MeLa-BitChute dataset can be found at: https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/KRD1VS.

The MeLa BitChute Dataset

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.

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

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.

BelElect: a New Dataset for Bias Research from a "Dark" Platform

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Next from ICWSM 2022

NELA-Local: A Dataset of U.S. Local News Articles for the Study of County-level News Ecosystems

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