Mitra, T., Wright, G. P., & Gilbert, E. (2017, February). A parsimonious language model of social media credibility across disparate events. In Proceedings of the 2017 ACM Conference on Computer Supported Cooperative Work and Social Computing (pp. 126-145). ACM.
Today, people can easily share literally everything they want through social networks and a huge amount of data is produced by them every day. But how can we distinguish true information from rumors in social networks? To address the information credibility problem in social networks, the authors in this paper, built a model to predict perceived credibility from language using a corpus of Twitter messages called CREDBANK.
As the authors mentioned, three parts have been defined for the information credibility that are message credibility, media credibility and source credibility. The authors state that their proposed credibility assessment focuses more on the information quality and they did not consider source credibility. Referring to several studies, they express their reasons for emphasizing on linguistic markers instead of source of information. But some questions came to my mind when I read their reasons. They quoted from Sundar that “it is next to impossible for an average Internet user to have a well-defined sense of the credibility of various sources and message categories on the Web…”. I have no doubt about what Sunder mentioned, but if it is not possible for an average Internet user to evaluate the credibility of various sources, is it possible for that user to evaluate the content to assess credibility of the information? In my opinion, credibility of sources in social networks can be much easier than credibility of the content for an average Internet user.
Users usually trust a social media user who is more popular. For example, the more followers you have (popularity), the more trustable you are. To measure the popularity or in other words credibility of a user, there are several other approaches such as the number of retweets and mentions in Tweeter or the number of viewers and likes in Facebook (and YouTube). I agree with Sundar when he talks about multiple layers of source in social networks, but I think most of the time popular users share reliable information. So, even if, for example, a tweet has been handed several times, it’s possible to assess the credibility of the tweet by evaluating the users who have retweeted that tweet.
I have been also thinking about using these approaches to spot fake reviews. The existence of fake reviews even in Amazon or Yelp is undeniable. Although Amazon repeatedly claims that more than 99 percent of its users’ reviews are real (written by real users) but several reliable researches show something else.
There are many websites in the Internet where sellers are looking for shoppers to give positive feedback in exchange for money or other compensation. The existence of the paid reviews has made customers suspicious about the credibility of the reviews. One approach to spot the fake reviews is evaluating the credibility of the sources (reviewers). Does a given reviewer leave only positive reviews? Do they tend to focus on products from unknown companies? Ranking users on their credibility can be considered as a solution to evaluate the credibility of the reviews. Amazon has done this approach by awarding badges to customers based on the type of their contributions in Amazon.com such as sharing reviews frequently. However, it seems that their solutions have not been sufficient.
I think employing the same approach demonstrated in this paper, to spot the fake reviews can be useful. I still believe that source credibility has a very important role in the information credibility, however, can we have a better evaluation of the information by combining these two approaches together?
