Individuals increasingly spend a significant portion of their lives online, and the Internet serves as a primary medium for obtaining and disseminating information. This online information shapes beliefs, attitudes, civic action, and even societal divides. A complex network of people, content, information sources, technologies, and platforms (online structures) shapes how information is created, shared, and consumed on the Internet. Yet, due to a variety of reasons such as lack of feasible operationalization or appropriate data, studies of online information often neglect the social nature of information as well as the cross-platform experiences that define the modern Internet, resulting in a limited, platform- and content-specific understanding of online information, removed from its social context. In addition, any understanding of the online information experience today needs to reckon with the impact of generative AI, and this impact also needs to be understood in the context of different information-seeking behaviours facilitated by different kinds of platforms. In this talk, I will address these limitations by first discussing how mainstream news articles get used to support misleading narratives on social media, using network science and natural language processing methods. Next, I will discuss ongoing research work that increases our understanding of the impact of generative AI on information-seeking behavior, especially in terms of web referrals. I will then briefly discuss ongoing and future research work tracking differences and consistencies in information consumption experiences for the same end-users across different online platforms. Through new research contributions that incorporate the social nature of information, study the impact of generative AI, and analyze the cross-platform experiences of the same set of Internet users, this talk aims to broaden the scope of our understanding of the modern online information ecosystem.

Pranav Goel is a Postdoctoral Research Associate at Northeastern University’s Network Science Institute. He earned his PhD in Computer Science at the University of Maryland in 2023. His research interests broadly span natural language processing and computational social science, and he specializes in using web and text data as a potent digital trace of societal dynamics to empirically investigate interdisciplinary research questions. He is currently interested in investigating the impact of generative AI on online information-seeking behavior, building a cross-platform understanding of online information consumption including generative AI as a component of the broader information ecosystem, and the sociopolitical phenomena of framing and narratives in news and social media. His work has been published in major computer science conferences such as NeurIPS, EMNLP, and ICWSM, as well as interdisciplinary journals with a broad audience, such as Nature Human Behaviour and Nature Scientific Data.