Journalists and Twitter: A Muti-dimensional Quantitative Description of Usage Patterns – Mossaab Bagdouri
Summary
Twitter is a great platform for journalists and news organizations to reach out and interact with their audiences. In this paper, Bagdouri studies the interaction of journalists / news organizations and their audience by surveying 18 features of three different account categories (journalists, news organizations, and news consumers), across two languages and cultural backgrounds (English-speaking and Arab-speaking countries), and three types of news media (print, radio, and television). By performing Welch’s t-test and Kolmogrov-Smirnov test with these features and different categories of Twitter accounts, Bagdouri found that journalists tend to target and have personal engagements with their audience, whereas news organizations prefer broadcasting their posts and are more official. The same pattern is found when comparing Arab journalists and English journalists, where Arab journalists appear to broadcast more tweets and more distinguishable from their audience than English journalists. The paper also finds that journalists across different media types are very different.
Reflection
This is an overall very interesting paper. I really like how Bagdouri compares journalists from different cultural backgrounds and how he compares journalists/news organizations with their audiences. These comparisons are probably not done in previous work, since this research surveys the largest set of twitter accounts and tweets and is more focused on journalists. However, the paper’s definition for “audience” intrigues me. “Audience” in this paper are accounts that “have a bidirectional follower / friend relationship” with selected journalists. The limitation that the paper addresses is that some of these audience accounts might include other journalists, but since the number of journalists is statistically insignificant compare to the total number, this isn’t really a limitation. This definition of “audience” omits twitter accounts that follow these journalists and don’t have a bidirectional follower relationship, and these twitter accounts are true audience of these journalists in my opinion. People that journalists follow may include other journalists and their friends in real life, and they are not representative of what the actual audience perception and reaction are like. I think it would be better to survey all the followers, instead of just the ones with bidirectional relationship with the journalists.
Another thing that intrigues me is that the journalists are not separated into news categories. Sports journalists and news organizations might tweet differently and have different interactions with their audience than the ones that cover politics. Audience might also react to different categories of news differently. For example, I often see sports fans tweeting out their excitement or disappointment with original tweets, which means less interaction with the journalists, and people react to political figures’ tweets or news by retweeting the original tweet extensively. Analyzing different news categories of journalists and news organizations can definitely be developed as part of the future work of this research.
This research can also consider a third type of users, Opinion-ist (writers who write Opinions). Opinion-ist are not journalists, but they still talk about news and are often quite influential. It would be interesting to compare journalists and opinion-ist to see how similar they are and how personal/targeting they are, since the research already showed that journalists are more targeting and relatable than news organization accounts.
Since this research can be seen as an extension of De Choudhury, Diakopoulos, and Naaman (2012)’s classifier research, an account classifier that’s based on Bagdouri’s data would be interesting. This classifier will make the verification process easier and suggest more targeted accounts to new users, and it should be able to at least identify whether an account belongs to a journalist, a news organization, or a news consumer, and what language they primarily speak. If more analysis is done with different news categories, the classifier should also be able to categorize different accounts.
