Counting the clicks on Twitter : A study in understanding click behavior on Twitter

Abstract: Social media has a large impact on our society. News articles are often accessed and shared through different social media sites . In fact, today the most common way to enter a website is from social medias. However, due to technical restrictions in what information these sites make public, it is often not possible to access click information from social medias. This complicates the analysis of popularity dynamics of news articles, for example. In this thesis, we work around that problem. By using an URL shortener service API, we can extract information about the clicks from the API. We will only look at content that is shared on Twitter because they have the friendliest view on sharing data for research purposes. To test this methodology we are doing a small prestudy in which we look at how biased news articles are shared on Twitter compared to more objective content. There are three parts in investigating the biased content. The first part is to extract Bitly links from Twitter. The second part is to examine the links and decide if it is a news article. Finally, we determine if the news article is biased. For this third step, we use two different approaches. First, we build a computational linguistics tool called a Naive Bayes classifier from already classified training data. Second, we classify different articles as articles with biased content or not, where an article is considered biased if the domain it resides on has a high content of biased articles. Our analysis of a sample data set that we have collected over a week showed that biased content is clicked for a longer period of time compared to non-biased content.