United States

Hate speech has proliferated on social media platforms in recent years. While this has been the focus of many studies, most works have exclusively focused on a single language, generally English. Low-resourced languages have been neglected due to the dearth of labeled resources. These languages, however represent an important portion of the data due to the multilingual nature of social media. This work presents a novel zero-shot, cross-lingual transfer learning pipeline based on pseudo-label fine-tuning of Transformer Language Models for automatic hate speech detection. We employ our pipeline on benchmark datasets covering English (source) and 6 different non-English (target) languages written in 3 different scripts. Our pipeline achieves an average improvement of 7.6% (in terms of macro-F1) over previous zero-shot, cross-lingual models. This demonstrates the feasibility of high accuracy automatic hate speech detection for low-resource languages. We release our code and models at https://github.com/&lt;redacted&gt;.

ICWSM 2022

Improving Zero-shot Cross-lingual Hate Speech Detection with pseudo-label fine-tuning of Transformer Language Models

cross-lingual learning

zero-shot

hate speech detection

**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).


**ICWSM Code of Conduct**

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Additionally, participants are encouraged to be courteous when sharing screen captures and photographs of conference events. Seek permission when possible and respect requests to take down images if those featured ask. Concerns around code of conduct or inclusion may be sent to icwsm22@aaai.org. If you have any concerns or items to report, please reach out to the General Chairs: Diyi Yang and Yelena Mejova.<br><br>


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.

Hate speech has proliferated on social media platforms in recent years. While this has been the focus of many studies, most works have exclusively focused on a single language, generally English. Low-resourced languages have been neglected due to the dearth of labeled resources. These languages, however represent an important portion of the data due to the multilingual nature of social media. This work presents a novel zero-shot, cross-lingual transfer learning pipeline based on pseudo-label fine-tuning of Transformer Language Models for automatic hate speech detection. We employ our pipeline on benchmark datasets covering English (source) and 6 different non-English (target) languages written in 3 different scripts. Our pipeline achieves an average improvement of 7.6% (in terms of macro-F1) over previous zero-shot, cross-lingual models. This demonstrates the feasibility of high accuracy automatic hate speech detection for low-resource languages. We release our code and models at https://github.com/<redacted>.

poster

A conversation corpus is essential to build interactive AI applications. 
However, participants' demographic information is relatively understudied mainly due to a lack of individual data. 
In this work, we analyzed a  Korean nationwide daily conversation corpus constructed by the National Institute of Korean Language (NIKL) to characterize the participation of different demographic groups in the corpus.

Who is Missing? Characterizing Participation of Different Demographic Groups in a Korean Nationwide Daily Conversation Corpus

We introduce the Reddit Politosphere, a large-scale resource of online political discourse covering more than 600 political discussion groups over a period of 12 years. It is to the best of our knowledge the largest and ideologically most comprehensive dataset of its type now available. One key feature of the Reddit Politosphere is that it consists of both text and network data, allowing for methodologically-diverse analyses. We describe in detail how we create the Reddit Politosphere, present descriptive statistics, and sketch potential directions for future research based on the resource.

The Reddit Politosphere: A Large-Scale Text and Network Resource of Online Political Discourse

A collection of over 3000 pages of emails sent by Anthony Fauci and his staff were released in an effort to understand the United States government response to the COVID-19 pandemic. In this paper, we describe how the original PDF document of emails was translated into a resource consisting of json files that make many future studies easy. We include examples for how to convert this email information into a network, a hypergraph, a temporal sequence, and a tensor for subsequent analysis, and discuss use cases and benefits in analyzing the data in these different derived formats. These resources are broadly useful for future research and pedagogical uses in terms of human and system behavioral interactions.

Fauci-email: a json Digest of Anthony Fauci's Released Emails

Currently, publicly available models for website classification do not offer an embedding method and have limited support for languages beyond English. We release a dataset with more than 1M websites in 92 languages with relative labels collected from Curlie, the largest multilingual crowdsourced Web directory. The dataset contains 14 website categories aligned across languages. Alongside it, we introduce Homepage2Vec, a machine-learned pre-trained model for classifying and embedding websites based on their homepage in a language-agnostic way. Homepage2Vec, thanks to its feature set (textual content, metadata tags, and visual attributes) and recent progress in natural language representation, is language-independent by design and can generate embeddings representation. We show that Homepage2Vec correctly classifies websites with a macro-averaged F1-score of 0.90, with stable performance across low- as well as high-resource languages. Feature analysis shows that a small subset of efficiently computable features suffices to achieve high performance even with limited computational resources. 
We make publicly available the curated Curlie dataset aligned across languages, the pre-trained Homepage2Vec model, and libraries.

Language-Agnostic Website Embedding and Classification

Despite the increasing limitations for unvaccinated people, in many European countries there is still a non-negligible fraction of individuals who refuse to get vaccinated against SARS-CoV-2, undermining governmental efforts to eradicate the virus. We study the role of online social media in influencing individuals' opinion towards getting vaccinated by designing a large-scale collection of Twitter messages in three different languages -- French, German, Italian -- and providing public access to the data collected. Focusing on the European context, our VaccinEU dataset aims to help researchers to better understand the impact of online (mis)information about vaccines and design more accurate communication strategies to maximize vaccination coverage.

VaccinEU: COVID-19 vaccine conversations on Twitter in French, German and Italian

This paper proposes a large-scale and comprehensive dataset of 28 sub-datasets of state-backed tweets and accounts affiliated with 14 different countries, spanning more than 3 years, and a corresponding "negative" dataset of background tweets from the same time period and on similar topics. To our knowledge, this is the first dataset that contains both state-sponsored propaganda tweets and carefully collected corresponding negative tweet datasets for so many countries spanning such a long period of time.

A Large-scale Longitudinal Multimodal Dataset of State-backed Information Operations on Twitter

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.

MMCHIVED: Developing Multimodal Chile and Venezuela Event 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.

Improving Zero-shot Cross-lingual Hate Speech Detection with pseudo-label fine-tuning of Transformer Language Models

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Next from ICWSM 2022

Who is Missing? Characterizing Participation of Different Demographic Groups in a Korean Nationwide Daily Conversation Corpus

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