Assessing the Effects of Social Familiarity and Stance Similarity in Interaction Dynamics

Dey, Kuntal; Shrivastava, Ritvik; Kaushik, Saroj; Mathur, Vaibhav

doi:10.1007/978-3-319-72150-7_68

Cited by 5 publications

(7 citation statements)

References 26 publications

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“…One of the elements that has been extensively used to infer Twitter users' stances is the retweet [17,[83][84][85]. Another element that has been heavily investigated is the hashtag; this element has been used in literature to infer similarity between users to predict the stance [37,86]. The work of [86] used the soft cosine similarity feature to gauge the similarity between the users that post under the same hashtag.…”

Section: Network Featuresmentioning

confidence: 99%

“…Another element that has been heavily investigated is the hashtag; this element has been used in literature to infer similarity between users to predict the stance [37,86]. The work of [86] used the soft cosine similarity feature to gauge the similarity between the users that post under the same hashtag. The work of [37] used graph reinforcement to calculate the similarity between the users that post under the same hashtag.…”

Section: Network Featuresmentioning

confidence: 99%

See 1 more Smart Citation

Stance detection on social media: State of the art and trends

Aldayel

Magdy

2021

Information Processing & Management

155

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Section: Network Featuresmentioning

confidence: 99%

Section: Network Featuresmentioning

confidence: 99%

Stance detection on social media: State of the art and trends

Aldayel

Magdy

2021

Information Processing & Management

155

View full text Add to dashboard Cite

“…One of theses elements that has been extensively used to infer twitter user's stance is the retweet [Borge-Holthoefer et al 2015;Darwish et al 2017c;Weber et al 2013;Rajadesingan and Liu 2014]. Another element that has been heavily investigated is hashtags, this element has been used in literature to infer similarity between users in order to predict the stance [Darwish et al 2017a;Dey et al 2017]. The work of [Dey et al 2017] used soft cosine similarity to gauge the similarity between the users who post on the same hashtags.…”

Section: Network Featuresmentioning

confidence: 99%

“…Another element that has been heavily investigated is hashtags, this element has been used in literature to infer similarity between users in order to predict the stance [Darwish et al 2017a;Dey et al 2017]. The work of [Dey et al 2017] used soft cosine similarity to gauge the similarity between the users who post on the same hashtags. The work of [Darwish et al 2017a] used graph reinforcement to calculate the similarity between the users who post on the same hashtags.…”

Section: Network Featuresmentioning

confidence: 99%

Stance Detection on Social Media: State of the Art and Trends

AlDayel,

Magdy

2020

Preprint

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Stance detection on social media is an emerging opinion mining paradigm for various social and political applications where sentiment analysis might be sub-optimal. This paper surveys the work on stance detection and situates its usage within current opinion mining techniques in social media. An exhaustive review of stance detection techniques on social media is presented, including the task definition, the different types of targets in stance detection, the features set used, and the various machine learning approaches applied. The survey reports the state-of-the-art results on the existing benchmark datasets on stance detection, and discusses the most effective approaches.In addition, this study explores the emerging trends and the different applications of stance detection on social media. The study concludes by providing discussion of the gaps in the current existing research and highlighting the possible future directions for stance detection on social media.

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“…It has been identified in sociology and psychology studies that individuals' opinions are significantly affected by social relations and online contents [9,18,27]. Accordingly, some studies use contextual information from Twitter, such as hashtags or retweets, to solve the challenges mentioned above [14,38]. For example, Dey et al [14] propose a latent concept space to obtain the stance similarity using twitter hashtags for identifying stance.…”

Section: Introductionmentioning

confidence: 99%

Improving (Dis)agreement Detection with Inductive Social Relation Information From Comment-Reply Interactions

Luo¹,

Liu²,

Li³

et al. 2023

Preprint

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Dis)agreement detection aims to identify the authors' attitudes or positions (agree, disagree, neutral) towards a specific text. It is limited for existing methods merely using textual information for identifying (dis)agreements, especially for cross-domain settings. Social relation information can play an assistant role in the (dis)agreement task besides textual information. We propose a novel method to extract such relation information from (dis)agreement data into an inductive social relation graph, merely using the comment-reply pairs without any additional platform-specific information. The inductive social relation globally considers the historical discussion and the relation between authors. Textual information based on a pre-trained language model and social relation information encoded by pretrained RGCN are jointly considered for (dis)agreement detection. Experimental results show that our model achieves the state-of-theart performance for both the in-domain and cross-domain tasks on the benchmark -DEBAGREEMENT. We find social relations can boost the performance of the (dis)agreement detection model, especially for the long-token comment-reply pairs, demonstrating the effectiveness of the social relation graph. We also explore the effect of the knowledge graph embedding methods, the information fusing method, and the time interval in constructing the social relation graph, which shows the effectiveness of our model.

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Assessing the Effects of Social Familiarity and Stance Similarity in Interaction Dynamics

Cited by 5 publications

References 26 publications

Stance detection on social media: State of the art and trends

Stance detection on social media: State of the art and trends

Stance Detection on Social Media: State of the Art and Trends

Improving (Dis)agreement Detection with Inductive Social Relation Information From Comment-Reply Interactions

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