Vocabulary-Based Method for Quantifying Controversy in Social Media

Zarate, Juan Manuel Ortiz de; Feuerstein, Esteban

doi:10.1007/978-3-030-57855-8_12

Cited by 6 publications

(4 citation statements)

References 41 publications

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“…Although these approaches are language and domain-independent and can then be applied easily to any topic discussion, it nevertheless presents the drawback of not taking advantage of extra information. Some works attempted to overcome these limits by exploiting for instance named entities to infer the tendency nature (positive, negative, neutral) of users towards some given named entities [23], and user's vocabulary to cluster users with more similarities in their vocabularies [24]. Some recent works consider controversy detection as a graph classification problem [3].…”

Section: Controversy Detection and Quantificationmentioning

confidence: 99%

“…From the data we got access to, some tweets might be missing in our datasets, depending on the topic, as tweets could have been deleted since the last time it was retrieved in [4]. The resulting dataset consists of 30 topics with their number of tweets ranging from 5 458 to 36 716, involving a number of users ranging from 3 696 to 161 612 per topic [24]. Table 2 resumes the frequencies of tweets, re-tweets, users, and users with at least one published tweet in the corresponding topic.…”

Section: Datasetmentioning

confidence: 99%

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A SHAP-based controversy analysis through communities on Twitter

Benslimane,

Papastergiou,

Azé

et al. 2024

Preprint

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Controversy encompasses content that draws diverse perspectives, along with positive and negative feedback on a specific event, resulting in the formation of distinct user communities. Research on controversy can be broadly categorized into two domains: controversy detection/quantification, which aims to measure controversy on a topic, and controversy explainability, which seeks to comprehend the reasons behind a topic’s controversial nature. This paper primarily contributes to the realm of controversy explainability. We conduct an analysis of topic discussions on Twitter from a community perspective, investigating the role of text in accurately classifying tweets into their respective communities. To achieve this, we introduce a SHAP-based pipeline designed to quantify the influence of impactful text features on the predictions of three tweet classi-fiers. Our approach involves leveraging various text features, including BERT , TF − IDF, and LIWC. The results, derived from both SHAP plots and statistical analyses, distinctly reveal the substantial impact of certain text features in tweet classification. Furthermore, our findings underscore the significance of this study and underscore the potential advantages of combining text and user interactions for a comprehensive understanding of controversy quantification.

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Section: Controversy Detection and Quantificationmentioning

confidence: 99%

Section: Datasetmentioning

confidence: 99%

A SHAP-based controversy analysis through communities on Twitter

Benslimane,

Papastergiou,

Azé

et al. 2024

Preprint

View full text Add to dashboard Cite

show abstract

“…Although these approaches are language and domain-independent and can then be applied easily to any topic discussion, it nevertheless presents the drawback of not taking advantage of extra information. Some works attempted to overcome these limits by exploiting for instance named entities to infer the tendency nature (positive, negative, neutral) of users towards some given named entities [17], and user's vocabulary to cluster users with more similarities in their vocabularies [18]. Some recent works consider controversy detection as a graph classification problem [2].…”

Section: Controversy Detection and Quantificationmentioning

confidence: 99%

“…The resulting dataset consists of 30 topics with their number of tweets ranging from 5 458 to 36 716, involving a number of users ranging from 3 696 to 161 612 per topic. [18]. Textual features used to explain communities being topic dependent, we based the explainability section (section 3.4) on only 2 topics for simplification purposes, one being controversial (pelosi) and one non-controversial (thanksgiving).…”

Section: Datasetmentioning

confidence: 99%

Explaining controversy through community analysis on Twitter

Benslimane

Papastergiou

Azé

et al. 2023

International Database Engineered Applications Symposium Conference

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Controversy refers to content attracting different point-of-views, as well as positive and negative feedback on a specific event, gathering users into different communities. Research on controversy led to two main categories of works: controversy detection/quantification and controversy explainability. When the former aims to quantify controversy on a topic, the latter aims to understand why a topic is controversial or not. This paper mainly contributes to the controversy explainability. We analyze topic discussions on Twitter from the community perspective to investigate the power of text in classifying tweets into the right community. We propose a SHAP-based pipeline to quantify impactful text features on predictions of three tweet classifiers. We also rely on the use of different text features namely 𝐵𝐸𝑅𝑇 , 𝑇 𝐹 − 𝐼𝐷𝐹 , and 𝐿𝐼𝑊 𝐶. The results we obtain from both SHAP plots and statistical analysis show clearly significant impacts of some text features in classifying tweets.It also highlights the relevance of the study as well as the potential benefits of combining text and user interactions to quantify controversy.

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Controversy Detection: A Text and Graph Neural Network Based Approach

Benslimane

Azé

Bringay

et al. 2021

Lecture Notes in Computer Science

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Controversial content refers to any content that attracts both positive and negative feedback. Its automatic identification, especially on social media, is a challenging task as it should be done on a large number of continuously evolving posts, covering a large variety of topics. Most of the existing approaches rely on the graph structure of a topic-discussion and/or the content of messages. This paper proposes a controversy detection approach based on both graph structure of a discussion and text features. Our proposed approach relies on Graph Neural Network (gnn) to encode the graph representation (including its texts) in an embedding vector before performing a graph classification task. The latter will classify the post as controversial or not. Two controversy detection strategies are proposed. The first one is based on a hierarchical graph representation learning. Graph user nodes are embedded hierarchically and iteratively to compute the whole graph embedding vector. The second one is based on the attention mechanism, which allows each user node to give more or less importance to its neighbors when computing node embeddings. We conduct experiments to evaluate our approach using different real-world datasets. Conducted experiments show the positive impact of combining textual features and structural information in terms of performance.

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Vocabulary-Based Method for Quantifying Controversy in Social Media

Cited by 6 publications

References 41 publications

A SHAP-based controversy analysis through communities on Twitter

A SHAP-based controversy analysis through communities on Twitter

Explaining controversy through community analysis on Twitter

Controversy Detection: A Text and Graph Neural Network Based Approach

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