A Survey of Twitter Rumor Spreading Simulations

Serrano, Emilio; Iglesias, Carlos Á.; Garijo, Mercedes Ruiz

doi:10.1007/978-3-319-24069-5_11

Cited by 33 publications

(14 citation statements)

References 26 publications

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“…While many studies have explored the diffusion of rumours, an exhaustive analysis of these studies is not within the scope of this survey article, which focuses instead on research concerning development of approaches to detect and resolve rumours. To read more about studies looking at the diffusion of rumours, we recommend the surveys by Serrano et al (2015) and Walia and Bhatia (2016).…”

Section: Characterising Rumours: Understanding Rumour Diffusion and Fmentioning

confidence: 99%

Detection and Resolution of Rumours in Social Media

et al. 2018

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Despite the increasing use of social media platforms for information and news gathering, its unmoderated nature often leads to the emergence and spread of rumours, i.e., items of information that are unverified at the time of posting. At the same time, the openness of social media platforms provides opportunities to study how users share and discuss rumours, and to explore how to automatically assess their veracity, using natural language processing and data mining techniques. In this article, we introduce and discuss two types of rumours that circulate on social media: long-standing rumours that circulate for long periods of time, and newly emerging rumours spawned during fast-paced events such as breaking news, where reports are released piecemeal and often with an unverified status in their early stages. We provide an overview of research into social media rumours with the ultimate goal of developing a rumour classification system that consists of four components: rumour detection, rumour tracking, rumour stance classification, and rumour veracity classification. We delve into the approaches presented in the scientific literature for the development of each of these four components. We summarise the efforts and achievements so far toward the development of rumour classification systems and conclude with suggestions for avenues for future research in social media mining for the detection and resolution of rumours.

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Section: Characterising Rumours: Understanding Rumour Diffusion and Fmentioning

confidence: 99%

Detection and Resolution of Rumours in Social Media

et al. 2018

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“…Malicious spreading refers to spreading of information that is harmful to the community, including the spread of malicious programs (computer viruses, worms, trojan horses, spyware, etc. ), malvertising, rumors and gossips [13,74,75,76]. Through web services of the connected internet, such information can spread to millions of agents within a very short time.…”

Section: Malicious Spreadingmentioning

confidence: 99%

Dynamics of information diffusion and its applications on complex networks

et al. 2016

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The ongoing rapid expansion of the Word Wide Web (WWW) greatly increases the information of effective transmission from heterogeneous individuals to various systems. Extensive research for information diffusion is introduced by a broad range of communities including social and computer scientists, physicists, and interdisciplinary researchers. Despite substantial theoretical and empirical studies, unification and comparison of different theories and approaches are lacking, which impedes further advances. In this article, we review recent developments in information diffusion and discuss the major challenges. We compare and evaluate available models and algorithms to receptively investigate their physical roles and optimization designs. Potential impacts and future directions are discussed. We emphasize that information diffusion has great scientific depth and combines diverse research fields which makes it interesting for physicists as well as interdisciplinary researchers.

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“…Fake tweets, on the other hand, are the tweets which spread misinformation. Serrano et al [10] provided an extensive survey on fake tweet detection. Unlike spam tweets, fake tweets are mostly associated with major events, and the accounts that produce these fake contents are mostly created during these events [11], [12].…”

Section: Related Workmentioning

confidence: 99%

Multitask learning for blackmarket tweet detection

Arora

Paka

Chakraborty

2019

Proceedings of the 2019 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining

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Online social media platforms have made the world more connected than ever before, thereby making it easier for everyone to spread their content across a wide variety of audiences. Twitter is one such popular platform where people publish tweets to spread their messages to everyone. Twitter allows users to Retweet other users' tweets in order to broadcast it to their network. The more retweets a particular tweet gets, the faster it spreads. This creates incentives for people to obtain artificial growth in the reach of their tweets by using certain blackmarket services to gain inorganic appraisals for their content.In this paper, we attempt to detect such tweets that have been posted on these blackmarket services in order to gain artificially boosted retweets. We use a multitask learning framework to leverage soft parameter sharing between a classification and a regression based task on separate inputs. This allows us to effectively detect tweets that have been posted to these blackmarket services, achieving an F1-score of 0.89 when classifying tweets as blackmarket or genuine.

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A Survey of Twitter Rumor Spreading Simulations

Cited by 33 publications

References 26 publications

Detection and Resolution of Rumours in Social Media

Detection and Resolution of Rumours in Social Media

Dynamics of information diffusion and its applications on complex networks

Multitask learning for blackmarket tweet detection

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