Models and Methods for Dissemination of Information and Knowledge Online

At a broad level, we can view the participation of users in the process of dissemination as falling into one of two settings: collaborative or competitive. In the collaborative setting, the participants work together in crafting knowledge online, e.g., by asking questions and contributing answers,or by discussing news or opinion pieces. In contrast, as competitors, they vie for the attention of their followers on social media. This thesis investigates both these settings.


The first part of the thesis focuses on the understanding and analysis of content being created online collaboratively. To this end, I propose models for understanding the complexity of the content of collaborative online discussions by looking exclusively at the signals of agreement and disagreement expressed by the crowd. This leads to a formal notion of complexity of opinions and online discussions. Next, I turn my attention to the participants of the crowd, i.e., the creators and consumers themselves, and propose an intuitive model for both the evolution of their expertise and the value of the content they collaboratively contribute and learn from on online Q&A based forums.
The second part of the thesis explores the competitive setting. It provides methods to help creators gain more attention from their followers on social media. In particular, I consider the problem of controlling the timing of the posts of users with the aim of maximizing the attention that their posts receive under the idealized setting of full-knowledge of timing of posts of others. To solve it, I develop a general reinforcement learning based method which is shown to have good performance on the when-to-post problem and which can be employed in many other settings as well, e.g., determining the reviewing times for spaced repetition which lead to optimal learning.
The last part of the thesis looks at methods for relaxing the idealized assumption of full knowledge. This basic question of determining the visibility of one’s posts on the followers’ feeds becomes difficult to answer on the internet when constantly observing the feeds of all the followers becomes unscalable. I explore the relationship between this problem and the well-studied problem of web-crawling to update a search engine’s index and provide algorithms with performance guarantees for feed observation policies that minimize the error in the estimate of the visibility of one’s posts.