Modelling structure and predicting dynamics of discussion threads in online boards

Medvedev, Alexey; Delvenne, Jean‐Charles; Lambiotte, Renaud

doi:10.1093/comnet/cny010

Cited by 32 publications

(33 citation statements)

References 37 publications

(39 reference statements)

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“…Our study focuses on discussion threads, where the successive 'comment-to' relations indicate the information flows. From the view of network analysis, discussion threads can be naturally represented by tree structure, with nodes representing a post and the comments under it and edges representing comment-to actions [17,27,30]. We would apply network analysis in our study to depict several crucial structural properties from the constructed discussion cascades.…”

Section: Information Cascadesmentioning

confidence: 99%

Conspiracy vs science: A large-scale analysis of online discussion cascades

et al. 2021

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With the emergence and rapid proliferation of social media platforms and social networking sites, recent years have witnessed a surge of misinformation spreading in our daily life. Drawing on a large-scale dataset which covers more than 1.4M posts and 18M comments from an online social media platform, we investigate the propagation of two distinct narratives-(i) conspiracy information, whose claims are generally unsubstantiated and thus referred as misinformation to some extent, and (ii) scientific information, whose origins are generally readily identifiable and verifiable. We find that conspiracy cascades tend to propagate in a multigenerational branching process whereas science cascades are more likely to grow in a breadth-first manner. Specifically, conspiracy information triggers larger cascades, involves more users and generations, persists longer, and is more viral and bursty than science information. Content analysis reveals that conspiracy cascades contain more negative words and emotional words which convey anger, fear, disgust, surprise and trust. We also find that conspiracy cascades are much more concerned with political and controversial topics. After applying machine learning models, we achieve an AUC score of nearly 90% in discriminating conspiracy from science narratives using the constructed features.

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Section: Information Cascadesmentioning

confidence: 99%

Conspiracy vs science: A large-scale analysis of online discussion cascades

et al. 2021

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“…Due to their self-exciting nature, Hawkes processes have been used to model retweet cascades in social media (e. g., [22,23,38,39,63]). Here the findings provide consistent evidence that marked Hawkes processes are highly effective for modeling the spreading behavior of online information.…”

Section: Self-exciting Point Processesmentioning

confidence: 99%

Detecting False Rumors from Retweet Dynamics on Social Media

Naumzik,

Feuerriegel

2022

Preprint

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False rumors are known to have detrimental effects on society. To prevent the spread of false rumors, social media platforms such as Twitter must detect them early. In this work, we develop a novel probabilistic mixture model that classifies true vs. false rumors based on the underlying spreading process. Specifically, our model is the first to formalize the self-exciting nature of true vs. false retweeting processes. This results in a novel mixture marked Hawkes model (MMHM). Owing to this, our model obviates the need for feature engineering; instead, it directly models the spreading process in order to make inferences of whether online rumors are incorrect. Our evaluation is based on 13,650 retweet cascades of both true. vs. false rumors from Twitter. Our model recognizes false rumors with a balanced accuracy of 64.97 % and an AUC of 69.46 %. It outperforms state-of-the-art baselines (both neural and feature engineering) by a considerable margin but while being fully interpretable. Our work has direct implications for practitioners: it leverages the spreading process as an implicit quality signal and, based on it, detects false content. CCS CONCEPTS• Information systems → Social networks; Data mining; • Mathematics of computing → Stochastic processes.

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“…Various point process models have been proposed for predicting the final number of re-shares [19,21] and their temporal pattern [10] on social media. Furthermore, these models have been applied to interpret the endogenous and exogenous shocks to the activity on YouTube [22] and Twitter [23].…”

Section: Related Workmentioning

confidence: 99%

Modeling the spread of fake news on Twitter

et al. 2021

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Fake news can have a significant negative impact on society because of the growing use of mobile devices and the worldwide increase in Internet access. It is therefore essential to develop a simple mathematical model to understand the online dissemination of fake news. In this study, we propose a point process model of the spread of fake news on Twitter. The proposed model describes the spread of a fake news item as a two-stage process: initially, fake news spreads as a piece of ordinary news; then, when most users start recognizing the falsity of the news item, that itself spreads as another news story. We validate this model using two datasets of fake news items spread on Twitter. We show that the proposed model is superior to the current state-of-the-art methods in accurately predicting the evolution of the spread of a fake news item. Moreover, a text analysis suggests that our model appropriately infers the correction time, i.e., the moment when Twitter users start realizing the falsity of the news item. The proposed model contributes to understanding the dynamics of the spread of fake news on social media. Its ability to extract a compact representation of the spreading pattern could be useful in the detection and mitigation of fake news.

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Modelling structure and predicting dynamics of discussion threads in online boards

Cited by 32 publications

References 37 publications

Conspiracy vs science: A large-scale analysis of online discussion cascades

Conspiracy vs science: A large-scale analysis of online discussion cascades

Detecting False Rumors from Retweet Dynamics on Social Media

Modeling the spread of fake news on Twitter

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