Stance detection is important for understanding different attitudes and beliefs on the Internet. However, given that a passage's stance toward a given topic is often highly dependent on that topic, building a stance detection model that generalizes to unseen topics is difficult. In this work, we propose using contrastive learning as well as an unlabeled dataset of news articles that cover a variety of different topics to train topic-agnostic/TAG and topic-aware/TAW embeddings for use in downstream stance detection. Combining these embeddings in our full TATA model, we achieve state-of-the-art performance across several public stance detection datasets (0.771 $F_1$-score on the Zero-shot VAST dataset). We release our code and data at https://github.com/hanshanley/tata.

TATA: Stance Detection via Topic-Agnostic and Topic-Aware Embeddings

QAnon is a far-right conspiracy theory whose followers largely organize online. In this work, we seek to identify, understand, and learn from the websites that spread QAnon content online. Using website crawling seeded from two of the largest QAnon hotbeds on the Internet, Voat and 8kun, and the resulting hyperlink graph, we curate the largest list of QAnon centered websites to date. From this list of 324 QAnon focused sites, we document the different types of QAnon-focused domains, their domain hosting, and their popularity. We show QAnon websites' connection to mainstream news and misinformation websites, highlighting the outsized role misinformation websites play in spreading the conspiracy. We finally leverage our hyperlink graph and build a random forest classifier to distinguish between misinformation and authentic news sites, achieving 0.98 AUC on a test set. Our results demonstrate new and effective ways to study conspiracy and misinformation on the Internet.

No Calm in The Storm: Investigating QAnon Website Relationships

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PS4 (In-person) Posters, Demo, Industry, Findings

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**Welcome to EMNLP 2023!**

On behalf of the EMNLP 2023 Organizing Committee, I extend a warm and heartfelt welcome to all of you to the 2023 Conference on Empirical Methods in Natural Language Processing (EMNLP). It is with immense pleasure and excitement that we gather here in Singapore, a vibrant hub of innovation and technological advancement.

The conference program is packed with insightful presentations, thought-provoking workshops, and engaging networking opportunities. In addition to the technical sessions, we have also planned several social events that will provide you with the opportunity to connect with your colleagues.

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Yuji Matsumoto (RIKEN AIP) 
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EMNLP 2023

EMNLP 2023 took place in Singapore from Dec 6th to Dec 10th, 2023.

s1-3: Misinformation/Conspiracy

technical paper

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ICWSM 2022

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.

Hans W. A. Hanley

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SHORT BIO

Presentations

TATA: Stance Detection via Topic-Agnostic and Topic-Aware Embeddings

No Calm in The Storm: Investigating QAnon Website Relationships

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