Graph-Based Conversation Analysis in Social Media

Social media platforms offer their audience the possibility to reply to posts through comments and reactions. This allows social media users to express their ideas and opinions on shared content, thus opening virtual discussions. Most studies on social networks have focused only on user relationships or on the shared content, while ignoring the valuable information hidden in the digital conversations, in terms of structure of the discussion and relation between contents, which is essential for understanding online communication behavior. This work proposes a graph-based framework to assess the shape and structure of online conversations. The analysis was composed of two main stages: intent analysis and network generation. Users' intention was detected using keyword-based classification, followed by the implementation of machine learning-based classification algorithms for uncategorized comments. Afterwards, human-in-the-loop was involved in improving the keyword-based classification. To extract essential information on social media communication patterns among the users, we built conversation graphs using a directed multigraph network and we show our model at work in two real-life experiments. The first experiment used data from a real social media challenge and it was able to categorize 90% of comments with 98% accuracy. The second experiment focused on COVID vaccine-related discussions in online forums and investigated the stance and sentiment to understand how the comments are affected by their parent discussion. Finally, the most popular online discussion patterns were mined and interpreted. We see that the dynamics obtained from conversation graphs are similar to traditional communication activities

Online discussions through the lens of interaction patterns

Computer-mediated communication is arguably prevailing over face-to-face. However, many of the subtleties that make in-person communication personal, cues such as an ironic tone of voice or an effortless posture, are inherently impossible to render through a screen. The context vanishes from the conversation - what is left is therefore mostly text, enlivened by occasional multimedia. At least, this seems the dominant opinion of both industry and academia, that recently focused considerable resources on a deeper understanding of natural and visual language. I argue instead that richer cues are missing from online interaction only because current applications do not acknowledge them -- indeed, communication online is already infused with nonverbal codes, and the effort needed to leverage them is well worth the amount of information they carry. This dissertation therefore focuses on what is left out of the traditional definition of content: I refer to these aspects of communication as content-agnostic. Specifically, this dissertation makes three contributions. First, I formalize what constitutes content-agnostic information in computer-mediated communication, and prove content-agnostic information is as personal to each user as its offline counterpart. For this reason, I choose as a venue of research the web forum, a supposedly text-based, impersonal communication environment, and show that it is possible to attribute a message to the corresponding author solely on the basis of its content-agnostic features -- in other words, without looking at the content of the message at all. Next, I display how abundant and how varied is the content-agnostic information that lies untapped in current applications.To this end, I analyze the content-agnostic aspects of one type of interaction, the quote, and draw conclusions on how these may support discussion, signal user status, mark relationships between users, and characterize the discussion forum as a community. One interesting implication is that discussion platforms may not need to introduce new features for supporting social signals, and conversely social networks may better integrate discussion by enhancing its content-agnostic qualities. Finally, I demonstrate how content-agnostic information reveals user behavior. I focus specifically on trolls, malicious users that disrupt communities through deceptive or manipulative actions. In fact, the language of trolls blends in with that of civil users in heated discussions, which makes collecting irrefutable evidence of trolling difficult even for human moderators. Nonetheless, I show that a combination of content-agnostic and linguistic features sets apart discussions that will eventually be trolled, and reactions to trolling posts. This provides evidence of how content-agnostic information can offer a point of view on user behavior that is at the same time different from, and complementary to, that offered by the actual content of the contribution. Popular up and coming platforms, such as Snapchat, Tumblr, or Yik Yak, are increasingly abandoning persistent, threaded, text-based discussion, in favor of ephemeral, loosely structured, mixed-media content. Although the results of this dissertation are mostly drawn from discussion forums, its research frame and methods should apply directly to these other venues, and to a broad range of communication paradigms. Also, this is but a preliminary step towards a fuller understanding of what additional cues can or should complement content to overcome the limitations of computer-mediated communication