Developing a Precise Approach to Identifying Inciting Speech Online - Cori Faklaris' blog - HeyCori

The discourse around social media moderation often centers on the idea of “censorship” and protecting free expression vs. protecting conversation health via account bans (what my generation dubbed “Facebook jail”). This framing can make the choices in moderation seem binary. However, for those of us either navigating or studying polarized opinions in online spaces, it … Continue reading "Developing a Precise Approach to Identifying Inciting Speech Online"

The Jade Gateway to Trust: How Socio-Cultural Perspectives Shape Trust in Chinese NFT Communities

What does a 2,500-year-old philosophy have to do with the latest digital assets? As it turns out, everything — especially when it comes to…

EasyHypergraph: an open-source software for fast and memory-saving analysis and learning of higher-order networks - Humanities and Social Sciences Communications

Higher-order relationships exist widely across different disciplines. In the realm of real-world systems, significant interactions involving multiple entities are common. The traditional pairwise modeling approach leads to the loss of important higher-order structures, while hypergraph is one of the most typical representations of higher-order relationships. To deeply explore the higher-order relationships, researchers and practitioners use hypergraph analysis to model the higher-order relationships and describe the important topological features in higher-order networks. At the same time, they carry out hypergraph learning studies to learn better node representations by designing hypergraph neural network models. However, existing hypergraph libraries still have the following research gaps. The first is that most of them are not able to support both hypergraph analysis and hypergraph learning, which negatively impacts the user experience. The second is that the existing libraries exhibit insufficient computational performance, which causes researchers and practitioners to spend more time and incur expensive resource costs. To fill these research gaps, we present EasyHypergraph, a comprehensive, computationally efficient, and storage-saving hypergraph computational library. To ensure comprehensiveness, EasyHypergraph designs data structures to support both hypergraph analysis and hypergraph learning. To ensure fast computation and efficient memory utilization, EasyHypergraph designs the computational workflow and demonstrates its effectiveness. Through experiments on five typical hypergraph datasets, EasyHypergraph saves at most 8470 s and 935 s over two baseline libraries in terms of analyzing node distance on a dataset with more than one hundred thousand nodes. For hypergraph learning, EasyHypergraph reduces HGNN training time by approximately 70.37% in a similar scenario. Finally, by conducting case studies for hypergraph analysis and learning, EasyHypergraph exhibits its usefulness in social science research.

HCII Seminar Series - David Widder | Human-Computer Interaction Institute

David Gray Widder (he/him) studies how people creating artificial intelligence systems think about the downstream harms their systems make possible, and the wider cultural, technical, and economic logics which shape these thoughts. He is currently a Postdoctoral Fellow at the Digital Life Initiative at Cornell Tech, an affiliate of the Data & Society research institute, and a research fellow of the European AI & Society Fund.

Notice Detail - SciOpen

HCII Seminar Series - Amy Bruckman | Human-Computer Interaction Institute

Amy Bruckman is Regents’ Professor in the School of Interactive Computing at the Georgia Institute of Technology. Her research focuses on social computing, with interests in online collaboration, CSCW, and content moderation. Bruckman received her Ph.D. from the MIT Media Lab in 1997, and a B.A. in physics from Harvard University in 1987. She is a Fellow of The ACM and a member of the SIGCHI Academy. She is the author of the book “Should You Believe Wikipedia? Online Communities and the Construction of Knowledge” (2022).

James Ashford (@[email protected])

Calling all #academics #scientists and #researchers of Mastodon. In the context of social media and the role of the Fediverse, what areas of research are poorly understood or under-researched? What are consider up-and-coming and novel? #socialmedia #research #mastodon #fediverse #socialcomputing #socialnetwork #study