The rumour spectrum

Turenne, Nicolas

doi:10.1371/journal.pone.0189080

Cited by 26 publications

(4 citation statements)

References 32 publications

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“…The 'comprehension' of rumour spreading (21%), discusses how the propagation of rumours happen i.e. : the propagation characteristics of online social networks rumours, propagation behaviour, the pattern and the trend of propagation [66]- [69]. It is worth mentioning that this type of effort mostly contributes to a model, as a way of mitigating cyber propaganda.…”

Section: Discussionmentioning

confidence: 99%

Socio-Technical Mitigation Effort to Combat Cyber Propaganda: A Systematic Literature Mapping

et al. 2020

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This systematic mapping literature aims to identify current research and directions for future studies in terms of combating cyber propaganda in the social media, which is used by both human effort and technological approaches (socio-technical) for mitigation. Out of 5176 retrieved articles, only 98 of them were selected for primary studies; classified based on research artifacts, mitigation effort, and the social media platforms involved in the research. The search was conducted using selected databases and applying selection criteria set for this research. Through the analysis, important research trends were identified based on human effort and technological approaches in mitigating and combating the cyber-propaganda issues. The authors also identified various mitigation socio-technical approaches such as identification, detection, image recognition, prediction, truth discovery and comprehension of rumours flow. The study also highlights areas for further improvements, to complement the performances of existing techniques. Besides, the study provides a brief review of cyber propaganda detection using classification techniques. Hence, it has set forth applicable research focus on the areas dealing with the mitigation of risk borne by cyber propaganda in the social media. INDEX TERMS Systematic mapping review, social media, cyber propaganda, mitigation, human effort, socio-technical approaches.

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Section: Discussionmentioning

confidence: 99%

Socio-Technical Mitigation Effort to Combat Cyber Propaganda: A Systematic Literature Mapping

et al. 2020

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“…Based on relevant debate-related French hashtags, either for the COVID-19 vaccine or the Ukraine conflict debate, and a random tweet corpus 57 , we trained a two-class classifier based on BertTweetFR 58 . This classifier allowed us to keep only elite users’ tweets dealing with the selected debates.…”

Section: Methodsmentioning

confidence: 99%

Gaining a better understanding of online polarization by approaching it as a dynamic process

Treuillier,

Castagnos,

Lagier

et al. 2024

Sci Rep

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Polarization is often a cliché, its conceptualization remains approximate and no consensus has been reached so far. Often simply seen as an inevitable result of the use of social networks, polarization cannot be viewed solely from an ideological or affective perspective. We propose to better understand the issue polarization by approaching it as a sequential process, drawing on a dual expertise in political and data sciences. We compare the polarization process between one mature debate (COVID-19 vaccine) and one emerging debate (Ukraine conflict) at the time of data collection. Both debates are studied on Twitter users, a highly politicized population, and on the French population to provide key elements beyond the traditional US context. This unprecedented analysis confirms that polarization varies over time, through a succession of specific periods, whose existence and duration depend on the maturity of the debate. Importantly, we highlight that polarization is paced by context-related events. Bearing this in mind, we pave the way for a new generation of personalized depolarization strategies, adapted to the context and maturity of debates.

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“…Early studies of rumour detection focus on supervised learning algorithms incorporating engineered features from post contents, user profiles as well as information propagation patterns (Castillo et al, 2011;Liu et al, 2015;Kwon et al, 2013;Ma et al, 2015;Rath et al, 2017). Turenne (2018) analysed lexical content and information propagation based on Allport's theory of transmission (Allport and Postman, 1947). It identified 53 features within six categories to represent a rumour message, from semantic meaning to information transmission.…”

Section: Related Workmentioning

confidence: 99%

DUCK: Rumour Detection on Social Media by Modelling User and Comment Propagation Networks

Lin¹,

Zhang²,

Lau³

2022

Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Langua

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Social media rumours, a form of misinformation, can mislead the public and cause significant economic and social disruption. Motivated by the observation that the user network -which captures who engages with a storyand the comment network -which captures how they react to it -provide complementary signals for rumour detection. In this paper, we propose DUCK (rumour detection with user and comment networks) for rumour detection on social media. We study how to leverage transformers and graph attention networks to jointly model the contents and the structure of social media conversations, as well as the network of users who engage in these conversations. Over four widely used benchmark rumour datasets in English and Chinese, we show that DUCK produces superior performance for detecting rumours, creating a new state-of-the-art.

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The rumour spectrum

Cited by 26 publications

References 32 publications

Socio-Technical Mitigation Effort to Combat Cyber Propaganda: A Systematic Literature Mapping

Socio-Technical Mitigation Effort to Combat Cyber Propaganda: A Systematic Literature Mapping

Gaining a better understanding of online polarization by approaching it as a dynamic process

DUCK: Rumour Detection on Social Media by Modelling User and Comment Propagation Networks

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