Here, I highlight several representative publications to demonstrate my approach to analyzing social phenomena. I also include hands-on tutorials with specific codes and toy data on the Tutorial page.
Li. Y., & Bond, R. M. (2022). Examining semantic (dis)similarity in news through news organizations' ideological similarity, similarity in truthfulness, and public engagement on social media: A network approach. Human Communication Research. https://doi.org/10.1093/hcr/hqac020
The rise of homogenization and polarization in the news may inhibit individuals’ understanding of an issue and the functioning of a democratic society. This study applies a network approach to understanding patterns of semantic similarity and divergence across news coverage. Specifically, we focus on how (a) inter-organizational networks based on media ideology, (b) inter-organizational networks based on news truthfulness, and (c) public engagement that news articles received on social media may affect semantic similarity in the news. We use large-scale user logs data on social media platforms (i.e., Facebook and Twitter) and news text data from more than 100 news organizations over 10 months to examine the three potential processes. Our results show that the similarity between news organizations in terms of media ideology and news truthfulness is positively associated with semantic similarity, whereas the public engagement that news articles received on social media is negatively associated with semantic similarity. Our study contributes to theory development in mass communication by shifting to a network paradigm that connects news organizations, news content, and news audiences. We demonstrate how scholars across communication disciplines may collaborate to integrate distinct theories, connect multiple levels, and link otherwise separate dimensions. Methodologically, we demonstrate how synchronizing network science with natural language processing and combining social media log data with text data can help to answer research questions that communication scholars are interested in. The findings’ implications for news polarization are discussed.
"Cultural networks" describe the relationships between news organizations that are inferred from the semantic similarity of the news content they produce. We conceptualize the conceptualization of "cultural networks" based on Bail (2016) and build the networks using Bail's textnet package in R.
We applied the Additive and Multiplicative Effects Network Moldes (Hoff, 2021) to analyze the longitudinal network data using the amen package in R.
Li, Y., & Bond, R. M. (2022). Evidence of the persistence and consistency of social signatures. Applied Network Science, 7(1), 1–19. https://doi.org/10.1007/s41109-022-00448-0
Human social networks are composed of multiple dynamic and overlapping communication networks, in which membership changes over time. However, less well understood are whether and how our communication patterns are similar or different over time and across various modes of communication. Here, we use data on the frequency of phone calls, text messages, and in-person interactions to examine the social signatures of more than 700 students in a university setting. Our analysis shows that although there is substantial turnover in participants’ networks, participants’ social signatures are persistent across time and consistent across communication modes. Further, we find that communication networks that are mediated via phone calls or text messages are more stable than are in-person networks. Our results show that, likely due to limitations in cognitive and emotional resources, people maintain networks of relatively stable size and structure their communication within those networks in predictable patterns. Our findings may help with formalizing social network theories, explaining individual-level attitudes and behaviors and aggregate-level social phenomena, and making predictions and detecting abnormalities in applied fields.