8 citations · 31 across the 10 of their papers we have counts for
15 papers
Influence Ranking Improvement via Link Addition in Social Networks
Taiki Ikemoto, Sho Tsugawa
Social media platforms increasingly rely on influential users for information dissemination in domains such as marketing and political campaigns. As the value of being recognized a…
Interplay between Emotional Dynamics and Network Structure on the Social Media Vent
Yuina Takahashi, Sho Tsugawa
The sharing and contagion of emotions on social media influence online interactions and users' psychological states. This study analyzes emotional dynamics on Vent, an emotion-shar…
Effects of Transcript Compression on LLM-based Medical Misinformation Detection in Japanese YouTube Videos
Yuya Wake, Sho Tsugawa, Toshiyuki Amagasa
Large language models (LLMs) are increasingly used to assess long-form medical videos, but their effectiveness may depend on whether transcripts are provided in full or compressed…
Converging Naming Styles, Persistent Network Locality: GitHub in the LLM Era
Yuto Tamura, Sho Tsugawa
Social conventions often emerge through repeated interactions within social networks, allowing shared practices to coexist with variation across groups. Large language models (LLMs…
Learning Inflation Narratives from Reddit: How Lightweight LLMs Reveal Forward-Looking Economic Signals
Ryuichi Saito, Sho Tsugawa
Public perceptions and expectations of inflation shape household spending, wage bargaining, and policy support, making them key determinants of macroeconomic outcomes. However, cur…
Empirical Evaluation of Link Deletion Methods for Limiting Information Diffusion on Social Media
Shiori Furukawa, Sho Tsugawa
Although beneficial information abounds on social media, the dissemination of harmful information such as so-called ``fake news'' has become a serious issue. Therefore, many resear…
Identifying Stable Influencers: Distinguishing Stable and Temporal Influencers Using Long-Term Twitter Data
Harutaka Yamada, Sho Tsugawa, Mitsuo Yoshida
For effective social media marketing, identifying stable influencers-those who sustain their influence over an extended period-is more valuable than focusing on users who are influ…
Contrastive Cascade Graph Learning for Classifying Real and Synthetic Information Diffusion Patterns
Naoki Shibao, Sho Tsugawa
A wide variety of information is disseminated through social media, and content that spreads at scale can have tangible effects on the real world. To curb the spread of harmful con…
Revisiting Information Diffusion Beyond Explicit Social Ties: A Study of Implicit-Link Diffusion on Twitter
Yuto Tamura, Sho Tsugawa, Kohei Watabe
Information diffusion on social media platforms is often assumed to occur primarily through explicit social connections, such as follower or friend ties. However, information frequ…
Exploring Unknown Social Networks for Discovering Hidden Nodes
Sho Tsugawa, Hiroyuki Ohsaki
In this paper, we address the challenge of discovering hidden nodes in unknown social networks, formulating three types of hidden-node discovery problems, namely, Sybil-node discov…
Do Large Language Models Advocate for Inferentialism?
Yuzuki Arai, Sho Tsugawa
The emergence of large language models (LLMs) such as ChatGPT and Claude presents new challenges for philosophy of language, particularly regarding the nature of linguistic meaning…
An Accurate Graph Generative Model with Tunable Features
Takahiro Yokoyama, Yoshiki Sato, Sho Tsugawa +1
A graph is a very common and powerful data structure used for modeling communication and social networks. Models that generate graphs with arbitrary features are important basic te…
Identifying Influential Brokers on Social Media from Social Network Structure
Sho Tsugawa, Kohei Watabe
Identifying influencers in a given social network has become an important research problem for various applications, including accelerating the spread of information in viral marke…
GraphTune: A Learning-based Graph Generative Model with Tunable Structural Features
Kohei Watabe, Shohei Nakazawa, Yoshiki Sato +2
Generative models for graphs have been actively studied for decades, and they have a wide range of applications. Recently, learning-based graph generation that reproduces real-worl…
A Tunable Model for Graph Generation Using LSTM and Conditional VAE
Shohei Nakazawa, Yoshiki Sato, Kenji Nakagawa +2
With the development of graph applications, generative models for graphs have been more crucial. Classically, stochastic models that generate graphs with a pre-defined probability…