Twitter Bot Detection

In this thesis, I explore the identification of non-human Twitter users. I am interested in classifying users by behavior into the categories of either bot or human. My goal in this research is to find an accurate and efficient means of identifying and segregating non-human Twitter users from their human counterparts. I use a two-stage data collection process to collect Twitter users suspected of being a bot and then obtain a majority vote on the suspected users to validate the suspicion. I gather, on average, 1000 tweets per user, on which I calculate 40 features characterizing the user. I explore the effectiveness of three different methods to most accurately classify users as either a bot or a human based on these features. The results of this work show that bots can be classified efficiently and with a high degree of accuracy. I show that certain features play a larger role in the classification process than others. The applications of Twitter bot identification include: (i) protecting users from malicious content (ii) spam filtering, and (iii) bot removal from Twitter data for other research

Charting the Constellation of Science Reform

Over the past decade, a sense of urgency has been building in the scientific community. They have discovered that much of the literature body is unreliable and possibly invalid thanks to weak theory, flawed methods, and shoddy statistics. This is driven by a widespread competitive, secretive approach to research, which, in turn, is fueled by toxic academic incentive structures. Many in the community have decided to address these issues, coming together in what has become known as the ‘scientific reform movement’. While these ‘reformers’ are often spoken of a single, homogeneous entity, my findings underscore the heterogeneity of the reform community. In my dissertation, I explore the scientific reform group using ethnography and social network analysis tools. I primarily studied their online Twitter engagements to understand their culture, practices, and structure. With Wenger’s Community of Practice theory as an interpretive framework, I analyze scientific reform discourse playing out between reformers on Twitter. Using quantitative Twitter friend/follow data, I investigate which reform members engage online, using following behavior to understand aspects of their social structure. I link the quantitative exploration with my qualitative analysis, to conclude that while the reformers are united by their interest in improving science, they are better characterized as a constellation of small communities of practice, each with their own norms, priorities, and unique approach to the group enterprise of scientific reform. My investigation is an exercise in reflexivity as I have studied a community in which I am an active part