Predicting Human Activities from User-Generated Content
The activities we do are linked to our interests, personality, political preferences, and decisions we make about the future. In this paper, we explore the task of predicting human activities from user-generated content. We collect a dataset containing instances of social media users writing about a range of everyday activities. We then use a state-of-the-art sentence embedding framework tailored to recognize the semantics of human activities and perform an automatic clustering of these activities. We train a neural network model to make predictions about which clusters contain activities that were performed by a given user based on the text of their previous posts and self-description. Additionally, we explore the degree to which incorporating inferred user traits into our model helps with this prediction task.
Code (0)
등록된 구현이 없습니다.
Tasks
ClusteringSentenceSentence EmbeddingSentence-EmbeddingSimilar Papers 제목 키워드 기반
Semantic Enrichment of Mobile Phone Data Records Using Background Knowledge
Every day, billions of mobile network events (i.e. CDRs) are generated by cellular phone operator companies. Latent in this data are inspiring insights about human actions and behaviors, the discovery of which is importa…
EVOLVE: Predicting User Evolution and Network Dynamics in Social Media Using Fine-Tuned GPT-like Model
Social media platforms are extensively used for sharing personal emotions, daily activities, and various life events, keeping people updated with the latest happenings. From the moment a user creates an account, they con…
DecoderRecommendation SystemsModeling Human Responses to Multimodal AI Content
As AI-generated content becomes widespread, so does the risk of misinformation. While prior research has primarily focused on identifying whether content is authentic, much less is known about how such content influences…
Predicting the Level of Text Standardness in User-generated Content
ReHAR: Robust and Efficient Human Activity Recognition
Designing a scheme that can achieve a good performance in predicting single person activities and group activities is a challenging task. In this paper, we propose a novel robust and efficient human activity recognition …
Activity RecognitionHuman Activity RecognitionOptical Flow Estimation