United States

This paper introduces the Multimodal Chile \&amp; Venezuela Protest Event Dataset (MMCHIVED).  MMCHIVED generates city-day event data using a new source of data, text and images shared on social media.  These data enables the improved measurement of theoretically important variables such as protest size, protester and state violence, protester demographics, and emotions.  In Venezuela, MMCHIVED records many more protests than existing datasets.  In Chile, it records slightly more events than the Armed Conflict Location and Events Dataset (ACLED).  These extra events are from small cities far from Caracas and Santiago, an improvement of coverage over datasets that rely on newspapers, and the paper confirms they are true positives. While MMCHIVED covers protest events in Chile and Venezuela, the approach used in the paper is generalizable and could generate protest event data in 107 countries containing 97.14\% of global GDP and 82.7\% of the world&#39;s population.

ICWSM 2022

MMCHIVED: Developing Multimodal Chile and Venezuela Event Data

venezuela

chile

protest

emotion

social media

[![](https://assets.underline.io/uploads/markdown_image/1/image/81a3c0317d24f663b49b996875024d45.png)](https://aclanthology.org/volumes/2022.coling-1/)



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First established in 1965, the biennial COLING conference is held in diverse parts of the globe and attracts participants from both top-ranked research centers and emerging countries. Today, the most important developments in our field are taking place not only in universities and academic research institutes but also in industrial research departments including tech-startups. COLING provides opportunities for all these communities to showcase their exciting discovery.

In fall of 2022, COLING will be held in Gyeongju in a hybrid format. All participants can either present at the venue site or join virtually. As more people get vaccinated, we are happy to provide safer environments for our colleagues. We believe that COLING 2022 will be one of the conferences, free from the pandemic. The hybrid format gives presenters and sponsors a valuable opportunity to promote their companies in both an online and in-person venue. For the first time in a long time, customers can interact with their sponsor's products first-hand. The online venue, too, gives sponsors the chance to network with those unable to attend the in-person session.

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COLING 2022

COLING, the International Conference on Computational Linguistics, is one of the premier conferences for the natural language processing and computational linguistics.

This paper introduces the Multimodal Chile \& Venezuela Protest Event Dataset (MMCHIVED).  MMCHIVED generates city-day event data using a new source of data, text and images shared on social media.  These data enables the improved measurement of theoretically important variables such as protest size, protester and state violence, protester demographics, and emotions.  In Venezuela, MMCHIVED records many more protests than existing datasets.  In Chile, it records slightly more events than the Armed Conflict Location and Events Dataset (ACLED).  These extra events are from small cities far from Caracas and Santiago, an improvement of coverage over datasets that rely on newspapers, and the paper confirms they are true positives. While MMCHIVED covers protest events in Chile and Venezuela, the approach used in the paper is generalizable and could generate protest event data in 107 countries containing 97.14\% of global GDP and 82.7\% of the world's population.

poster

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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.

Recent research has shown how strongly Wikipedia and other web services or platforms are connected. For example, search engines rely heavily on surfacing Wikipedia links to satisfy their users' information needs and volunteer-created Wikipedia content frequently gets re-used on other social media platforms like Reddit. However, publicly accessible datasets that enable researchers to study the interrelationship between Wikipedia and other platforms are sparse. In addition to that, most studies only focus on certain points in time and don't consider the historical perspective. To begin solving these problems we developed TWikiL, the Twitter Wikipedia Link Dataset, which contains all Wikipedia links posted on Twitter in the period 2006 to January 2021. We extract Wikipedia links from Tweets and enrich the referenced articles with their respective Wikidata identifiers and Wikipedia topic categories, which will make this dataset immediately useful for a large range of scholarly use cases. In this paper, we describe the data collection process, perform an initial exploratory analysis and present a comprehensive overview of how this dataset can be useful for the research community.

TWikiL - The Twitter Wikipedia Link Dataset

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

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.

MMCHIVED: Developing Multimodal Chile and Venezuela Event Data

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TWikiL - The Twitter Wikipedia Link Dataset

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