Tweet, but Verify: Epistemic Study of Information Verification on Twitter

While Twitter provides an unprecedented opportunity to learn about breaking news and current events as they happen, it often produces skepticism among users as not all the information is accurate but also hoaxes are sometimes spread. While avoiding the diffusion of hoaxes is a major concern during fast-paced events such as natural disasters, the study of how users trust and verify information from tweets in these contexts has received little attention so far. We survey users on credibility perceptions regarding witness pictures posted on Twitter related to Hurricane Sandy. By examining credibility perceptions on features suggested for information verification in the field of Epistemology, we evaluate their accuracy in determining whether pictures were real or fake compared to professional evaluations performed by experts. Our study unveils insight about tweet presentation, as well as features that users should look at when assessing the veracity of tweets in the context of fast-paced events. Some of our main findings include that while author details not readily available on Twitter feeds should be emphasized in order to facilitate verification of tweets, showing multiple tweets corroborating a fact misleads users to trusting what actually is a hoax. We contrast some of the behavioral patterns found on tweets with literature in Psychology research.Comment: Pre-print of paper accepted to Social Network Analysis and Mining (Springer

Automated Credibility Assessment on Twitter

n this paper, we make a practical approach to automated credibility assessment on Twitter. We describe the process behind the design of an automated classifier for information credibility assessment. As an addition, we propose practical implementation of TwitterBOT, a tool which is able to score submitted tweets while working in the native Twitter interface

Automatically applying a credibility appraisal tool to track vaccination-related communications shared on social media

Background: Tools used to appraise the credibility of health information are time-consuming to apply and require context-specific expertise, limiting their use for quickly identifying and mitigating the spread of misinformation as it emerges. Our aim was to estimate the proportion of vaccination-related posts on Twitter are likely to be misinformation, and how unevenly exposure to misinformation was distributed among Twitter users. Methods: Sampling from 144,878 vaccination-related web pages shared on Twitter between January 2017 and March 2018, we used a seven-point checklist adapted from two validated tools to appraise the credibility of a small subset of 474. These were used to train several classifiers (random forest, support vector machines, and a recurrent neural network with transfer learning), using the text from a web page to predict whether the information satisfies each of the seven criteria. Results: Applying the best performing classifier to the 144,878 web pages, we found that 14.4\% of relevant posts to text-based communications were linked to webpages of low credibility and made up 9.2\% of all potential vaccination-related exposures. However, the 100 most popular links to misinformation were potentially seen by between 2 million and 80 million Twitter users, and for a substantial sub-population of Twitter users engaging with vaccination-related information, links to misinformation appear to dominate the vaccination-related information to which they were exposed. Conclusions: We proposed a new method for automatically appraising the credibility of webpages based on a combination of validated checklist tools. The results suggest that an automatic credibility appraisal tool can be used to find populations at higher risk of exposure to misinformation or applied proactively to add friction to the sharing of low credibility vaccination information.Comment: 8 Pages, 5 Figure

Measuring information credibility in social media using combination of user profile and message content dimensions

Information credibility in social media is becoming the most important part of information sharing in the society. The literatures have shown that there is no labeling information credibility based on user competencies and their posted topics. This study increases the information credibility by adding new 17 features for Twitter and 49 features for Facebook. In the first step, we perform a labeling process based on user competencies and their posted topic to classify the users into two groups, credible and not credible users, regarding their posted topics. These approaches are evaluated over ten thousand samples of real-field data obtained from Twitter and Facebook networks using classification of Naive Bayes (NB), Support Vector Machine (SVM), Logistic Regression (Logit) and J48 algorithm (J48). With the proposed new features, the credibility of information provided in social media is increasing significantly indicated by better accuracy compared to the existing technique for all classifiers

User perception of information credibility of news on Twitter

In this paper, we examine user perception of credibility for news-related tweets. We conduct a user study on a crowd-sourcing platform to judge the credibility of such tweets. By analysing user judgments and comments, we find that eight features, including some that can not be automatically identified from tweets, are perceived by users as important for judging information credibility. Moreover, distinct features like link in tweet, display name and user belief consistently lead users to judge tweets as credible. We also find that users can not consistently judge or even misjudge the credibility for some tweets on politics news

Information credibility perception on Twitter

Information on Twitter is vast and varied. Readers must make their own judgements to determine the credibility of the great wealth of information presented on Twitter. This research aims to identify the factors that influence readers' judgements of the credibility of information on Twitter, especially news-related information. Both internal (within the Twitter platform) and external factors are studied in this research. User studies are conducted to collect readers' perceptions of the credibility of news-related tweets, Twitter features, and the impact of reader characteristics, such as a reader's demographic attributes, their personality and behaviour. Twitter readers are found to depend solely on surface tweet features in making these judgements such as the author's Twitter ID, pictures, or the number of retweets and likes, rather than the tweet's metadata as recommended in previous studies. In this study, surface features are related to cognitive heuristics. Cognitive heuristics are features that the mind uses as shortcuts for making quick evaluations such as deciding the credibility of tweets. There are three main types of cognitive heuristic features found on Twitter that readers use to determine credibility: endorsement, reputation and confirmation. This study finds that readers do not use only one single feature to make credibility judgements but rather a combination of features. External factors such as a reader's educational background and geolocation also have a significant positive correlation with their perceptions of a tweet's credibility. Readers with tertiary level education, or living in a certain location or environment, such as in a crisis or conflict area, are observed to be more careful in making credibility judgements. Readers who possess conscientiousness and openness to experience personality traits are also seen to be very cautious in their credibility judgements. Another insight provided by this research is the categorisation of readers' behaviours according to credibility perceptions on Twitter. The behavioural categorisations are defined by readers' behavioural reliance on Twitter's surface features when judging the credibility of tweets. The findings can assist social media authors in designing the surface features of their social media content in order to enhance the content's credibility. Furthermore, findings from this research can help in developing effective credibility evaluation systems by considering readers' personal characteristics

Correlation analysis of reader's demographics and tweet credibility perception

When searching on Twitter, readers have to determine the credibility level of tweets on their own. Previous work has mostly studied how the text content of tweets in uences credibility perception. In this paper, we study reader demographics and information credibility perception on Twitter. We nd reader's educational background and geolocation have signi cant correlation with credibility perception. Further investigation reveals that combinations of demographic attributes correlating with credibility perception are insigni cant. Despite di erences in demographics, readers nd features regarding topic keyword and the writing style of a tweet to be independently helpful in perceiving tweets' credibility. While previous studies reported the use of features independently, our result shows that readers use combination of features to help in making credibility perception of tweets