Information credibility on twitter
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Summary
There are measurable differences in the way messages propagate, that can be used to classify them automatically as credible or not credible, with precision and recall in the range of 70% to 80%.
- Type
- article
- Published
- 2011-03-28
- Cited by
- 2,618
- References
- 37
- OpenAlex
- https://openalex.org/W2084591134
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:5919237
Keywords
Credibility, Computer science, Social media, Microblogging, Information retrieval
References
- Detecting Spammers on Twitter
- From Obscurity to Prominence in Minutes: Political Speech and Real-Time Search
- A Little Bird Told Me, So I Didn't Believe It: Twitter, Credibility, and Issue Perceptions
- Blogs of Information: How Gender Cues and Individual Motivations Influence Perceptions of Credibility
- Twitter adoption and use in mass convergence and emergency events
- Every Blog Has Its Day: Politically-interested Internet Users' Perceptions of Blog Credibility
- TwitterMonitor: trend detection over the twitter stream
- TwitterStand: news in tweets
- Detecting controversial events from twitter
- Finding high-quality content in social media
- Robust dynamic classes revealed by measuring the response function of a social system
- Why we twitter: understanding microblogging usage and communities
- Chatter on the red: what hazards threat reveals about the social life of microblogged information
- Twitter under crisis: can we trust what we RT?
- "OMG, from here, I can see the flames!": a use case of mining location based social networks to acquire spatio-temporal data on forest fires
- Detecting Spam in a Twitter Network
- Microblogging during two natural hazards events: what twitter may contribute to situational awareness
- The elements of computer credibility
- Truthy: mapping the spread of astroturf in microblog streams
- What is Twitter, a social network or a news media?
Cited by
- The Role of Uncertainty, Awareness, and Trust in Visual Analytics
- Rumors, False Flags, and Digital Vigilantes: Misinformation on Twitter after the 2013 Boston Marathon Bombing
- Moving on Twitter: using episodic hotspot and drift analysis to detect and characterise spatial trajectories
- Ranked Activity Streams
- Tweeting for businesses: increasing the return on investment of social media by using links
- Using Link Analysis to Discover Interesting Messages Spread Across Twitter
- Towards a social media analytics platform: event detection and user profiling for twitter
- Detecting Malicious Content on Facebook
- Un état des lieux sur les données massives
- Modelling trends in social media - Application and analysis using randomgraphs
- Supporting users with credibility assessments of health Tweets
- Public Archaeology in a Digital Age
- Adaptive Big Data Analytics for Deceptive Review Detection in Online Social Media
- Reaching People
- Computational Linguistic Models of Deceptive Opinion Spam
- The Selective Exposure Hypothesis Revisited: Does Social Networking Make a Difference?
- Understanding the informational and normative influences of rumour diffusion via social media on conflicts escalation
- Recommendation Strategies Based on User-Generated Data
- Social Media Data Mining: A Social Network Analysis of Tweets During the Australian 2010-2011 Floods
- SIR-Extended Information Diffusion Model of False Rumor and its Prevention Strategy for Twitter
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