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Argumentation mining and stance classifica- tion were recently introduced as interesting tasks in text mining. In this paper, a novel framework for argument tagging based on topic modeling is proposed. …...

AbstractClassifying the stance expressed in online microblogging social media is an emerging problem in opinion mining. We propose a probabilistic approach to stance classification in tweets, which models stance, target …...

One may express favor (or disfavor) to- wards a target by using positive or neg- ative language. Here for the first time we present a dataset of tweets annotated for …...

Stance classification aims to identify, for a particular issue under discussion, whether the speaker or author of a conversational turn has Pro (Favor) or Con (Against) stance on the is- …...

In this paper, we compare feature-based and Neural Network-based approaches on the su- pervised stance classification task for tweets in SemEval-2016 Task 6 Subtask A (Moham- mad et al., 2016). …...

AbstractFor the competition SemEval-2017 we investigated the possibility of performing stance classification (support, deny, query or comment) for messages in Twitter conversation threads related to rumours. Stance classification is interesting …...

AbstractThe 2017 Fake News Challenge Stage 1 (FNC-1) shared task addressed a stance classification task as a crucial first step towards detecting fake news. To date, there is no in-depth …...

Computational argumentation aims to model arguments as a set of premises that either support each other or collectively support a conclusion. We prepare three datasets of text-hypothesis pairs with support-based …...

AbstractThe viral spread of false, unverified and misleading information on the Internet has attracted a heightened attention of an interdisciplinary research community on the phenomenon. This paper contributes to the …...

AbstractThis paper describes our system submitted to SemEval 2019 Task 7: RumourEval 2019: Determining Rumour Veracity and Support for Rumours, Subtask A (Gorrell et al., 2019). The challenge focused on …...

AbstractThe paper presents Columbia team’s participation in the SemEval 2019 Shared Task 7: RumourEval 2019. Detecting rumour on social networks has been a focus of research in recent years. Previous …...

AbstractSocial media plays a crucial role as the main resource news for information seekers online. However, the unmoderated feature of social media platforms lead to the emergence and spread of …...

AbstractAutomatically verifying rumorous information has become an important and challenging task in natural language processing and social media analytics. Previous studies reveal that people’s stances towards rumorous messages can provide …...

AbstractIn this study, we propose a new multi-task learning approach for rumor detection and stance classification tasks. This neural network model has a shared layer and two task specific layers. …...

AbstractControversial claims are abundant in online media and discussion forums. A better understanding of such claims requires analyzing them from different perspectives. Stance classification is a necessary step for inferring …...

AbstractLearning from social-media conversations has gained significant attention recently because of its applications in areas like rumor detection. In this research, we propose a new way to represent social-media conversations …...

Identifying argument components has become an important area of research in argument mining. When argument components are identified, they can not only be used for stance classification but also can …...

AbstractThe prevalent use of social media enables rapid spread of rumors on a massive scale, which leads to the emerging need of automatic rumor verification (RV). A number of previous …...

AbstractThe growing interest in argument mining and computational argumentation brings with it a plethora of Natural Language Understanding (NLU) tasks and corresponding datasets. However, as with many other NLU tasks, …...

AbstractWe use a deep bidirectional transformer to extract the Myers-Briggs personality type from user-generated data in a multi-label and multi-class classification setting. Our dataset is large and made up of …...

AbstractWe study the problem of performing automatic stance classification on social media with neural architectures such as BERT. Although these architectures deliver impressive results, their level is not yet comparable …...

AbstractTraditionally, a debate usually requires a manual preparation process, including reading plenty of articles, selecting the claims, identifying the stances of the claims, seeking the evidence for the claims, etc. …...

AbstractWe present Stanceosaurus, a new corpus of 28,033 tweets in English, Hindi and Arabic annotated with stance towards 250 misinformation claims. As far as we are aware, it is the …...

Public expression of vaccine related sentiment on social media platforms can be used in infor- mation surveillance applications to gain insight into vaccine hesitancy and its spread. Effective identification of …...

AbstractWe present a human-in-the-loop evaluation framework for fact-checking novel misinformation claims and identifying social media messages that support them. Our approach extracts check-worthy claims, which are aggregated and ranked for …...