Browsing by Subject "Stance detection"

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Open Access
Stance detection: a survey
(Association for Computing Machinery, 2020) Küçük, D.; Can, Fazlı
Automatic elicitation of semantic information from natural language texts is an important research problem with many practical application areas. Especially after the recent proliferation of online content through channels such as social media sites, news portals, and forums; solutions to problems such as sentiment analysis, sarcasm/controversy/veracity/rumour/fake news detection, and argument mining gained increasing impact and significance, revealed with large volumes of related scientific publications. In this article, we tackle an important problem from the same family and present a survey of stance detection in social media posts and (online) regular texts. Although stance detection is defined in different ways in different application settings, the most common definition is “automatic classification of the stance of the producer of a piece of text, towards a target, into one of these three classes: {Favor, Against, Neither}.” Our survey includes definitions of related problems and concepts, classifications of the proposed approaches so far, descriptions of the relevant datasets and tools, and related outstanding issues. Stance detection is a recent natural language processing topic with diverse application areas, and our survey article on this newly emerging topic will act as a significant resource for interested researchers and practitioners.
Open Access
Stance detection: concepts, approaches, resources, and outstanding issues
(Association for Computing Machinery, 2021-07-11) Küçük, Dilek; Can, Fazlı
Stance detection (also known as stance classification and stance prediction) is a problem related to social media analysis, natural language processing, and information retrieval, which aims to de termine the position of a person from a piece of text they produce, towards a target (a concept, idea, event, etc.) either explicitly speci fied in the text, or implied only. The output of the stance detection procedure is usually from this set: {Favor, Against, None}. In this tutorial, we will define the core concepts and research problems re lated to stance detection, present historical and contemporary ap proaches to stance detection, provide pointers to related resources (datasets and tools), and we will cover outstanding issues and ap plication areas of stance detection. As solutions to stance detection can contribute to significant tasks including trend analysis, opin ion surveys, user reviews, personalization, and predictions for ref erendums and elections, it will continue to stand as an important research problem, mostly on textual content currently, and partic ularly on social media. Finally, we believe that image and video content will commonly be the subject of stance detection research soon.
Open Access
A tutorial on stance detection
(Association for Computing Machinery, Inc, 2022) Küçük, Dilek; Can, Fazlı
Stance detection (also known as stance classification, stance prediction, and stance analysis) is a problem related to social media analysis, natural language processing, and information retrieval, which aims to determine the position of a person from a piece of text they produce, towards a target (a concept, idea, event, etc.) either explicitly specified in the text, or implied only. Common stance classes include Favor, Against, and None. In this tutorial, we will define the core concepts and other related research problems, present historical and contemporary approaches to stance detection (including shared tasks and tools employed), provide pointers to related datasets, and cover open research directions and application areas of stance detection. As solutions to stance detection can contribute to diverse applications including trend analysis, opinion surveys, user reviews, personalization, and predictions for referendums and elections, it will continue to stand as an important research problem, mostly on textual content currently, and particularly on Web content including social media.