Stance Detection and Fact Checking

We are interested in studying method to determine the attitude expressed in a text towards a topic (stance detection), such as determining if a tweet expresses a positive, negative or neutral stance towards a political entity. One additional challenge we are exploring is stance detection in a conversational context, where the stance depends on the context of the conversation. Fact checking using textual data can be framed very similarly, namely as if an evidence document agrees with, disagrees with or is topically unrelated to a headline or claim.

We are also researching the relationship between attitudes towards entities on social media and gender bias as part of a project funded by DFF.

Publications

The goal of stance detection is to determine the viewpoint expressed in a piece of text towards a target. These viewpoints or contexts …

Truth can vary over time. Therefore, fact-checking decisions on claim veracity should take into account temporal information of both …

Stance detection concerns the classification of a writer’s viewpoint towards a target. There are different task variants, e.g., …

Public trust in science depends on honest and factual communication of scientific papers. However, recent studies have demonstrated a …

Most work on scholarly document processing assumes that the information processed is trust-worthy and factually correct. However, this …

Recently, novel multi-hop models and datasets have been introduced to achieve more complex natural language reasoning with neural …

Detecting attitudes expressed in texts, also known as stance detection, has become an important task for the detection of false …

In this paper, we describe our participation in the TREC Health Misinformation Track 2020. We submitted 11 runs to the Total Recall …

Adversarial attacks reveal important vulnerabilities and flaws of trained models. One potent type of attack are universal adversarial …

A critical component of automatically combating misinformation is the detection of fact check-worthiness, i.e. determining if a piece …

This paper provides the first study of how fact checking explanations can be generated automatically based on available claim context, …

We contribute the largest publicly available dataset of naturally occurring factual claims for the purpose of automatic claim …

Digital media enables not only fast sharing of information, but also disinformation. One prominent case of an event leading to …

In online discussion fora, speakers often make arguments for or against something, say birth control, by highlighting certain aspects …

We combine multi-task learning and semisupervised learning by inducing a joint embedding space between disparate label spaces and …

Rumour stance classification, defined as classifying the stance of specific social media posts into one of supporting, denying, …

Identifying public misinformation is a complicated and challenging task. An important part of checking the veracity of a specific claim …

Rumour stance classification is a task that involves identifying the attitude of Twitter users towards the truthfulness of the rumour …

This paper describes team Turing’s submission to SemEval 2017 RumourEval: Determining rumour veracity and support for rumours …

Talks

On 1 September 2021, the DFF Sapere Aude project EXPANSE on ‘Learning to Explain Attitudes on Social Media’ is kicking off, …