Zobrazeno 1 - 10
of 25
pro vyhledávání: '"Mu, Yida"'
Research in natural language processing (NLP) for Computational Social Science (CSS) heavily relies on data from social media platforms. This data plays a crucial role in the development of models for analysing socio-linguistic phenomena within onlin
Externí odkaz:
http://arxiv.org/abs/2410.03545
Large language models (LLMs) with their strong zero-shot topic extraction capabilities offer an alternative to probabilistic topic modelling and closed-set topic classification approaches. As zero-shot topic extractors, LLMs are expected to understan
Externí odkaz:
http://arxiv.org/abs/2405.00611
Topic modelling, as a well-established unsupervised technique, has found extensive use in automatically detecting significant topics within a corpus of documents. However, classic topic modelling approaches (e.g., LDA) have certain drawbacks, such as
Externí odkaz:
http://arxiv.org/abs/2403.16248
In this paper, we address the limitations of the common data annotation and training methods for objective single-label classification tasks. Typically, when annotating such tasks annotators are only asked to provide a single label for each sample an
Externí odkaz:
http://arxiv.org/abs/2311.05265
The use of abusive language online has become an increasingly pervasive problem that damages both individuals and society, with effects ranging from psychological harm right through to escalation to real-life violence and even death. Machine learning
Externí odkaz:
http://arxiv.org/abs/2309.14146
A crucial aspect of a rumor detection model is its ability to generalize, particularly its ability to detect emerging, previously unknown rumors. Past research has indicated that content-based (i.e., using solely source posts as input) rumor detectio
Externí odkaz:
http://arxiv.org/abs/2309.11576
Autor:
Liang, Tianyu, Mu, Yida, Kim, Soonho, Kuate, Darline Larissa Kengne, Lang, Julie, Vos, Rob, Song, Xingyi
A large number of conflict events are affecting the world all the time. In order to analyse such conflict events effectively, this paper presents a Classification-Aware Neural Topic Model (CANTM-IA) for Conflict Information Classification and Topic D
Externí odkaz:
http://arxiv.org/abs/2308.15232
Autor:
Mu, Yida, Wu, Ben P., Thorne, William, Robinson, Ambrose, Aletras, Nikolaos, Scarton, Carolina, Bontcheva, Kalina, Song, Xingyi
Instruction-tuned Large Language Models (LLMs) have exhibited impressive language understanding and the capacity to generate responses that follow specific prompts. However, due to the computational demands associated with training these models, thei
Externí odkaz:
http://arxiv.org/abs/2305.14310
Autor:
Mu, Yida, Jiang, Ye, Heppell, Freddy, Singh, Iknoor, Scarton, Carolina, Bontcheva, Kalina, Song, Xingyi
The COVID-19 pandemic led to an infodemic where an overwhelming amount of COVID-19 related content was being disseminated at high velocity through social media. This made it challenging for citizens to differentiate between accurate and inaccurate in
Externí odkaz:
http://arxiv.org/abs/2304.04811
Previous studies have highlighted the importance of vaccination as an effective strategy to control the transmission of the COVID-19 virus. It is crucial for policymakers to have a comprehensive understanding of the public's stance towards vaccinatio
Externí odkaz:
http://arxiv.org/abs/2304.04806