Credibility ranking of tweets during high impact events

Abstract

Twitter has evolved from being a conversation or opinion sharing medium among friends into a platform to share and disseminate information about current events. Events in the real world create a corresponding spur of posts (tweets) on Twitter. Not all content posted on Twitter is trustworthy or useful in providing information about the event. In this paper, we analyzed the credibility of information in tweets corresponding to fourteen high impact news events of 2011 around the globe. From the data we analyzed, on average 30% of total tweets posted about an event contained situational information about the event while 14% was spam. Only 17% of the total tweets posted about the event contained situational awareness information that was credible. Using regression analysis, we identified the important content and sourced based features, which can predict the credibility of information in a tweet. Prominent content based features were number of unique characters, swear words, pronouns, and emoticons in a tweet, and user based features like the number of followers and length of username. We adopted a supervised machine learning and relevance feedback approach using the above features, to rank tweets according to their credibility score. The performance of our ranking algorithm significantly enhanced when we applied re-ranking strategy. Results show that extraction of credible information from Twitter can be automated with high confidence.

A review of volunteered geographic information quality assessment methods

“…While some people contribute meaningful information most others use these mediums to express personal opinions, moods, or for malicious aims such as bullying or trolling to harass other users. Gupta and Kumaraguru (2012) conducted a study to investigate how much information is credible and therefore useful, and how much information is spam, on Twitter. They found that 14% of Tweets collected for event analysis were spam, while 30% of the Tweets contained situational awareness information, out of which only 17% of the total tweets contained credible situational awareness information.…”

“…The work of Gupta and Kumaraguru (2012) is similar to Castillo et al (2011), and follows a supervised feature classification PageRank like method to propagate the credibility on a network of Twitter events. They use event graph-based optimization to enhance the trust analysis at each iteration that updates the credibility scores.…”

Geographic variability of Twitter usage characteristics during disaster events

“…Despite the sheer volume of data shared on Twitter, not every Tweet provides information and facts related to an event (Gupta and Kumaraguru 2012). Rather, trending topics on Twitter, including disasters, can provide an opportunity for spammers to share spams using keywords associated with trending topics and generate revenue (Benevenuto et al 2010).…”

“…However, Gupta and Kumaraguru (2012) aim to assess credibility at the level of individual Tweets and argue that assessing credibility at a trending topic level is insufficient as trending topic about an earthquake may be true but Tweets about misleading magnitude can question the credibility. They used message and source-based features to determine credibility of individual Tweets.…”

“…One important aspect of data quality is the credibility of a Tweet, that is to say how likely is it that the content is for example, accurate, authoritative, objective, and current (Gupta and Kumaraguru 2012). Since Tweets are user-generated data, produced for many different reasons, they are also associated with varying quality with respect to particular contexts (Senaratne et al 2017).…”