Political rumoring on Twitter during the 2012 US presidential election: Rumor diffusion and correction

Shin, Jieun; Jian, Lian; Driscoll, Kevin; Bar, François

doi:10.1177/1461444816634054

Cited by 154 publications

(93 citation statements)

References 49 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…These results contribute to the existing literature on attacks against public figures and journalists (Barlow and Awan, 2016;Johnen et al, 2018;Preuss et al, 2017;Shin et al, 2017), which is largely anecdotal and limited to women. Our results enrich this literature by a theoretically driven and quantitative comparison of the prevalence rate of three avoidance strategies for both genders and two contrasting explanations of gendered avoidance.…”

Section: Discussionsupporting

confidence: 65%

See 1 more Smart Citation

Female journalists under attack? Explaining gender differences in reactions to audiences’ attacks

Stahel

Schoen

2019

New Media & Society

View full text Add to dashboard Cite

The literature on public figures attacked by their audiences is unclear why female and male figures react differently to attacks. This study examines why female journalists are more likely than male journalists to use avoidance strategies as a reaction to online attacks. Avoidance includes limiting audience engagement, adapting reporting behavior, and thinking about quitting journalism. Drawing on social role theory and gender stereotypes, this study contrasts two explanatory hypotheses. The results, based on mediation analyses of online survey data of 637 journalists representative of Switzerland, show that women are more likely than men to use avoidance strategies because women are more stressed by attacks. This heightened stress is argued to result from differences in gender role socialization. In contrast, while women are somewhat more severely attacked than men, this cannot explain their greater probability of avoidance. Results contribute a theoretically and empirically rich explanation of gendered reactions to attacks.

show abstract

Section: Discussionsupporting

confidence: 65%

“…Generally, public figures such as politicians, celebrities, popular academics, and journalists have become highly exposed and accessible. This has made them easy targets for shaming, defamation, and trolling (Barlow and Awan, 2016;Johnen et al, 2018;Preuss et al, 2017;Shin et al, 2017).…”

Section: Introductionmentioning

confidence: 99%

Female journalists under attack? Explaining gender differences in reactions to audiences’ attacks

Stahel

Schoen

2019

New Media & Society

View full text Add to dashboard Cite

show abstract

“…This is also confirmed by [62], who concluded that rumor spreaders form strong partisan structures. Del Vicario also shows that different types of misinformation propagate differently.…”

Section: Misinformation Dynamicssupporting

confidence: 75%

Online Misinformation

Fernández

Alani

2018

Companion of the the Web Conference 2018 on the Web Conference 2018 - WWW '18

View full text Add to dashboard Cite

Misinformation has become a common part of our digital media environments and it is compromising the ability of our societies to form informed opinions. It generates misperceptions, which have affected the decision making processes in many domains, including economy, health, environment, and elections, among others. Misinformation and its generation, propagation, impact, and management is being studied through a variety of lenses (computer science, social science, journalism, psychology, etc.) since it widely affects multiple aspects of society. In this paper we analyse the phenomenon of misinformation from a technological point of view. We study the current socio-technical advancements towards addressing the problem, identify some of the key limitations of current technologies, and propose some ideas to target such limitations. The goal of this position paper is to reflect on the current state of the art and to stimulate discussions on the future design and development of algorithms, methodologies, and applications.

show abstract

“…It has also been suggested that corrections are usually issued by news organisations and they can be sometimes widely spread (Takayasu et al 2015;Arif et al 2016;Andrews et al 2016), especially if those corrections come from like-minded accounts (Hannak et al 2014) and occasionally even leading to deletion or unsharing of the original post (Frias-Martinez et al 2012). However, corrections do not always have the same effect as the original rumours (Lewandowsky et al 2012;Shin et al 2016;Starbird et al 2014), which reinforces the need to develop rumour classification systems that deal with newly emerging rumours.…”

Section: Characterising Rumours: Understanding Rumour Diffusion and Fmentioning

confidence: 99%

Detection and Resolution of Rumours in Social Media

et al. 2018

View full text Add to dashboard Cite

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.

show abstract

Political rumoring on Twitter during the 2012 US presidential election: Rumor diffusion and correction

Cited by 154 publications

References 49 publications

Female journalists under attack? Explaining gender differences in reactions to audiences’ attacks

Female journalists under attack? Explaining gender differences in reactions to audiences’ attacks

Online Misinformation

Detection and Resolution of Rumours in Social Media

Contact Info

Product

Resources

About