Profile Update: The Effects of Identity Disclosure on Network Connections and Language

Our social identities determine how we interact and engage with the world surrounding us. In online settings, individuals can make these identities explicit by including them in their public biography, possibly signaling a change to what is important to them and how they should be viewed. Here, we perform the first large-scale study on Twitter that examines behavioral changes following identity signal addition on Twitter profiles. Combining social networks with NLP and quasi-experimental analyses, we discover that after disclosing an identity on their profiles, users (1) generate more tweets containing language that aligns with their identity and (2) connect more to same-identity users. We also examine whether adding an identity signal increases the number of offensive replies and find that (3) the combined effect of disclosing identity via both tweets and profiles is associated with a reduced number of offensive replies from others

Analyzing the Engagement of Social Relationships During Life Event Shocks in Social Media

Individuals experiencing unexpected distressing events, shocks, often rely on their social network for support. While prior work has shown how social networks respond to shocks, these studies usually treat all ties equally, despite differences in the support provided by different social relationships. Here, we conduct a computational analysis on Twitter that examines how responses to online shocks differ by the relationship type of a user dyad. We introduce a new dataset of over 13K instances of individuals' self-reporting shock events on Twitter and construct networks of relationship-labeled dyadic interactions around these events. By examining behaviors across 110K replies to shocked users in a pseudo-causal analysis, we demonstrate relationship-specific patterns in response levels and topic shifts. We also show that while well-established social dimensions of closeness such as tie strength and structural embeddedness contribute to shock responsiveness, the degree of impact is highly dependent on relationship and shock types. Our findings indicate that social relationships contain highly distinctive characteristics in network interactions and that relationship-specific behaviors in online shock responses are unique from those of offline settings.Comment: Accepted to ICWSM 2023. 12 pages, 5 figures, 5 table

Do LLMs Understand Social Knowledge? Evaluating the Sociability of Large Language Models with SocKET Benchmark

Large language models (LLMs) have been shown to perform well at a variety of syntactic, discourse, and reasoning tasks. While LLMs are increasingly deployed in many forms including conversational agents that interact with humans, we lack a grounded benchmark to measure how well LLMs understand \textit{social} language. Here, we introduce a new theory-driven benchmark, SocKET, that contains 58 NLP tasks testing social knowledge which we group into five categories: humor & sarcasm, offensiveness, sentiment & emotion, and trustworthiness. In tests on the benchmark, we demonstrate that current models attain only moderate performance but reveal significant potential for task transfer among different types and categories of tasks, which were predicted from theory. Through zero-shot evaluations, we show that pretrained models already possess some innate but limited capabilities of social language understanding and training on one category of tasks can improve zero-shot testing on others. Our benchmark provides a systematic way to analyze model performance on an important dimension of language and points to clear room for improvement to build more socially-aware LLMs. The associated resources are released at https://github.com/minjechoi/SOCKET.Comment: 24 pages, 7 tables, 5 figure

Supplemental Material for “More than meets the tie: Examining the Role of Interpersonal Relationships in Social Networks”

This is the supplementary material for the paper “More than meets the tie: Examining the Role of Interpersonal Relationships in Social Networks” accepted by the International Conference of Web and Social Media (ICWSM'21).Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/167015/3/Supp_ICWSM21.pdfDescription of Supp_ICWSM21.pdf : Supplementary materialSEL

Footwear Design Crowdsourcing Platform Model For Strengthening Of the Competitiveness Of The Footwear Industry

The volumes of global footwear production and consumption have been steadily increasing. In particular, the income increases of China and Southeast Asian countries have led to the rapid growth of footwear production and consumption in Asia. However, while advanced countries still include footwear business as one of their growth engines, Korea regard it as stagnant or diminishing. However, beyond the category of simple manufacturing, footwear industry involves the integration of highly functional products, parts manufacturing, and marketing business, and includes expertise in the fields of design, materials, and epidemiology. The strengthening of the shoe industry is an important potential driver of the overall economy. The strengthened competiveness of the footwear industry will play an important role in the overall economic growth. Crowdsourcing is an approach that encourages the participation of specific communities or unspecified masses in a company’s production, service, or problem-solving processes to increase efficiency. To this end, this paper suggests the crowdsourcing platform model built through the integration of footwear design and IT for the ultimate enhancement of the competitiveness of the Korean footwear industry. Following this paper, a study about the practical development, application, and active use of such platform needs to be conducted. One limitation of this study is that the platform is yet to be developed or applied. Future research should focus on developing an actual platform and further studies in its application and vitalization

A Computational Analysis on the Role of Social Relationships in Online Communication and Information Diffusion

Social relationships play a crucial role in shaping daily conversations and information sharing within social networks, both in person and on online platforms like Twitter and Facebook. These platforms have become immensely popular for accessing a wide range of information. While previous studies have contributed to understanding the properties of social ties, less attention has been devoted to directly identifying the characteristics of individual social relationships and their influence on dyadic interactions in online social networks. In this dissertation, I present three computational studies to identify and analyze the key characteristics of social relationships within large online social networks. These studies seek to shed light on how social relationships impact interactions and information diffusion. The first study approaches relationships through the lens of social dimensions, such as conflict or trust, wherein a dyad exhibits varying levels of dimension strength. The findings indicate that the strength of inferred dimensions accurately represents the nature of social relationships in Twitter ties. Additionally, these inferred dimensions can reflect community-level outcomes, such as the stability of organizations or well-being indices. The second study proposes a novel method for identifying different types of interpersonal relationships using a combination of text- and network-based features. Linguistic and diurnal communication patterns are found to differ significantly among various types of relationships, and it is possible to build accurate classifier models for inferring categories of social relationships based on communication on Twitter. Moreover, incorporating information about these relationships enhances the performance of retweet prediction models. Building upon the relationship classification model developed in Study 2, the third and final study investigates the responses of dyads of users facing unexpected life-shock events. Interestingly, the research uncovers relationship-specific reactions to different types of shocks, providing valuable insights into how social ties are influenced during challenging times. The findings from the three computational studies provide a comprehensive understanding of the dynamics of social relationships in the digital age.PHDInformationUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/177746/1/minje_1.pd

Blind rhythmic source separation: Nonnegativity and repeatability

An unsupervised method is proposed aiming at extracting rhythmic sources from commercial polyphonic music whose number of chan-nels is limited to one. Commercial music signals are not usually provided with more than two channels while they often contain mul-tiple instruments including singing voice. Therefore, instead of us-ing conventional ways, such as modeling mixing environments or statistical characteristics, we should introduce other source-specific characteristics for separating or extracting the sources. In this pa-per, we concentrate on extracting rhythmic sources from the mixture with the other harmonic sources. An extension of nonnegative ma-trix factorization (NMF) is used to analyze multiple relationships between spectral and temporal properties in the given input matri-ces. Moreover, temporal repeatability of the rhythmic sound sources is implicated as common rhythmic property among segments of an input mixture signal. The proposed method shows acceptable, but not superior separation quality to the referred drum source separa-tion systems. However, it has better applicability due to its blind manner in separation. Index Terms — Nonnegative matrix factorization, rhythmic source separation, musical information researc