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1. More than Meets the Tie: Examining the Role of Interpersonal Relationships in Social Networks

   Choi, Minje; Budak, Ceren; Romero, Daniel; Jurgens, Davi (May 2021, International Conference on Web and Social Media)

   Topics in conversations depend in part on the type of interpersonal relationship between speakers, such as friendship, kinship, or romance. Identifying these relationships can provide a rich description of how individuals communicate and reveal how relationships influence the way people share information. Using a dataset of more than 9.6M dyads of Twitter users, we show how relationship types influence language use, topic diversity, communication frequencies, and diurnal patterns of conversations. These differences can be used to predict the relationship between two users, with the best predictive model achieving a macro F1 score of 0.70. We also demonstrate how relationship types influence communication dynamics through the task of predicting future retweets. Adding relationships as a feature to a strong baseline model increases the F1 and recall by 1% and 2%. The results of this study suggest relationship types have the potential to provide new insights into how communication and information diffusion occur in social networks. 

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2. Context and content of teaching conversations: exploring how to promote sharing of innovative teaching knowledge between science faculty

   https://doi.org/10.1186/s40594-022-00369-5  

   Lane, A. Kelly; Earl, Brittnee; Feola, Stephanie; Lewis, Jennifer E.; McAlpin, Jacob D.; Mertens, Karl; Shadle, Susan E.; Skvoretz, John; Ziker, John P.; Stains, Marilyne; et al (August 2022, International Journal of STEM Education)

   Abstract BackgroundChange strategies may leverage interpersonal relationships and conversations to spread teaching innovations among science faculty. Knowledge sharing refers to the process by which individuals transfer information and thereby spread innovative ideas within an organization. We use knowledge sharing as a lens for identifying factors that encourage productive teaching-related conversations between individuals, characterizing the context and content of these discussions, and understanding how peer interactions may shape instructional practices. In this study, we interview 19 science faculty using innovative teaching practices about the teaching-focused conversations they have with different discussion partners. ResultsThis qualitative study describes characteristics of the relationship between discussion partners, what they discuss with respect to teaching, the amount of help-seeking that occurs, and the perceived impacts of these conversations on their teaching. We highlight the role of office location and course overlap in bringing faculty together and characterize the range of topics they discuss, such as course delivery and teaching strategies. We note the tendency of faculty to seek out partners with relevant expertise and describe how faculty perceive their discussion partners to influence their instructional practices and personal affect. Finally, we elaborate on how these themes vary depending on the relationship between discussion partners. ConclusionsThe knowledge sharing framework provides a useful lens for investigating how various factors affect faculty conversations around teaching. Building on this framework, our results lead us to propose two hypotheses for how to promote sharing teaching knowledge among faculty, thereby identifying productive directions for further systematic inquiry. In particular, we propose that productive teaching conversations might be cultivated by fostering collaborative teaching partnerships and developing departmental structures to facilitate sharing of teaching expertise. We further suggest that social network theories and other examinations of faculty behavior can be useful approaches for researching the mechanisms that drive teaching reform. 

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3. Initial Investigations into the Link Between Spatial and Technical Communication Skills

   https://doi.org/10.18260/1-2--47624  

   Lynch, John W; Sorby, Sheryl A; Murphy, Teri J; Aller, Betsy M (June 2024, ASEE Conferences)

   ABET requires that all engineering graduates are able to effectively communicate technical information; however, industry leaders often lament the technical communication skills of our engineering student graduates. Despite years of concerted effort, at a national level, the situation does not appear to be improving. In contrast, the spatial skills of engineering students are typically well above average. A significant body of research has demonstrated the link between high spatial skills and success in engineering overall. But is there a link between high spatial skills and low technical communication skills for some of our students? In other words, are the high spatial skills of engineering students negatively correlated with their technical communication skills? This paper reports on a portion of a larger study examining the relationship between technical communication and spatial skills. Data for this study was collected from 90 first-year engineering students at a large midwestern university. Students were administered two tests of spatial ability and completed phonemic and semantic fluency tasks individually while being video recorded. The focus of this paper is on the relationship between spatial skills and these two types of fluency— phonemic and semantic. Phonemic fluency is defined as how well you can put words together to form a cohesive sentence or paragraph; semantic fluency is related to the size of your vocabulary. Both types of fluency likely influence a person’s ability to effectively communicate technical information. Preliminary findings suggest a weakly positive link between spatial skills and both types of fluency, which prompts further investigation into how technical communication abilities are evaluated and informs future research in the area. Implications for engineering education based on our findings are discussed in the paper. 

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4. A Graph Neural Network Approach for Product Relationship Prediction

   https://doi.org/10.1115/DETC2021-69462  

   Ahmed, Faez; Cui, Yaxin; Fu, Yan; Chen, Wei (August 2021, ASME 2021 International Design Engineering Technical Conferences & Computers and Information in Engineering Conference)

   Abstract Graph representation learning has revolutionized many artificial intelligence and machine learning tasks in recent years, ranging from combinatorial optimization, drug discovery, recommendation systems, image classification, social network analysis to natural language understanding. This paper shows their efficacy in modeling relationships between products and making predictions for unseen product networks. By representing products as nodes and their relationships as edges of a graph, we show how an inductive graph neural network approach, named GraphSAGE, can efficiently learn continuous representations for nodes and edges. These representations also capture product feature information such as price, brand, and engineering attributes. They are combined with a classification model for predicting the existence of a relationship between any two products. Using a case study of the Chinese car market, we find that our method yields double the F-1 score compared to an Exponential Random Graph Model-based method for predicting the co-consideration relationship between cars. While a vanilla Graph-SAGE requires a partial network to make predictions, we augment it with an ‘adjacency prediction model’ to circumvent this limitation. This enables us to predict product relationships when no neighborhood information is known. Finally, we demonstrate how a permutation-based interpretability analysis can provide insights on how design attributes impact the predictions of relationships between products. Overall, this work provides a systematic method to predict the relationships between products in a complex engineering system. 

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5. Political Elites in the Attention Economy: Visibility Over Civility and Credibility?

   https://doi.org/10.1609/icwsm.v19i1.35814  

   Biswas, Ahana; Lin, Yu-Ru; Tai, Yuehong Cassandra; Desmarais, Bruce A (June 2025, Proceedings of the International AAAI Conference on Web and Social Media)

   Elected officials have privileged roles in public communication. In contrast to national politicians, whose posting content is more likely to be closely scrutinized by a robust ecosystem of nationally focused media outlets, sub-national politicians are more likely to openly disseminate harmful content with limited media scrutiny. In this paper, we analyze the factors that explain the online visibility of over 6.5K unique state legislators in the US and how their visibility might be impacted by posting low-credibility or uncivil content. We conducted a study of posting on Twitter and Facebook (FB) during 2020-21 to analyze how legislators engage with users on these platforms. The results indicate that distributing content with low-credibility information attracts greater attention from users on FB and Twitter for Republicans. Conversely, posting content that is considered uncivil on Twitter receives less attention. A noticeable scarcity of posts containing uncivil content was observed on FB, which may be attributed to the different communication patterns of legislators on these platforms. In most cases, the effect is more pronounced among the most ideologically extreme legislators. Our research explores the influence exerted by state legislators on online political conversations, with Twitter and FB serving as case studies. Furthermore, it sheds light on the differences in the conduct of political actors on these platforms. This study contributes to a better understanding of the role that political figures play in shaping online political discourse. 

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