Papers with social network
SocAoG: Incremental Graph Parsing for Social Relation Inference in Dialogues (2021.acl-long)
Copied to clipboard
| Challenge: | Existing studies focus on identifying entities' relations from the semantics of dialogues-they utilize either the attention mechanism or a refined token graph to locate informative words. |
| Approach: | They propose a sequential structure prediction task to incrementally parse SocAoG for dynamic inference upon any incoming utterance. |
| Outcome: | Empirical results show that the proposed model infers social relations more accurately than the state-of-the-art methods. |
A Framework for Representing Language Acquisition in a Population Setting (P18-1)
Copied to clipboard
| Challenge: | Existing approaches to model language acquisition and social structure are ineffective because nobody can travel back in time or fit entire natural environments into a lab. |
| Approach: | They propose a new analytic framework which combines previous network models' ability to capture realistic social structure with more elegant computational properties. |
| Outcome: | The proposed framework is able to capture real social structure and integrate with existing models while being modular and extensible. |
Low-Resource Counterspeech Generation for Indic Languages: The Case of Bengali and Hindi (2024.findings-eacl)
Copied to clipboard
| Challenge: | NLP is a technique that generates counterspeech that “counters” the vicious tone of online abuse and dilutes/ameliorates their rippling effect over the social network. |
| Approach: | They propose to use neural architectures to generate counterspeech that can "counter" the vicious tone of online abuse and dilute/ameliorate their rippling effect over the social network. |
| Outcome: | The proposed model can generate counterspeech in monolingual setups and is more transferable when languages belong to the same language family. |
Twitter Homophily: Network Based Prediction of User’s Occupation (P19-1)
Copied to clipboard
| Challenge: | Existing approaches to predicting Twitter users' demographic attributes exploit, select, and combine various features generated from text and network to achieve the best performance. |
| Approach: | They extend existing Twitter occupational class prediction data set and exploit social network homophily to achieve competitive performance. |
| Outcome: | The proposed method achieves better performance on a dataset with a small fraction of the training data. |
Continuous Relational Diffusion Driven Topic Model with Multi-grained Text for Microblog (2024.lrec-main)
Copied to clipboard
| Challenge: | Existing topic models assume that there are only 0/1-state relationships between the two parties in social networks, but the relationship status in real life is more complicated. |
| Approach: | They propose a topic model that leverages unsupervised learning to mine hidden topics in document collections using multi-grained text. |
| Outcome: | The proposed model can be applied to microblog with multi-grained text to realize the representation of the relationship state and make up for the context and structural information lost by previous representation methods. |
The Stepwise Deception: Simulating the Evolution from True News to Fake News with LLM Agents (2025.emnlp-main)
Copied to clipboard
| Challenge: | Existing studies assume fake news is inherently existing rather than exploring its gradual formation. |
| Approach: | They propose a Large Language Model-based simulation approach explicitly focusing on fake news evolution from real news. |
| Outcome: | The proposed framework captures fake news evolution patterns and accurately reproduces known fake news, aligning closely with human evaluations. |