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With the rapid development of social media such as Twitter and Weibo,detecting keywords from a huge volume of text data streams in real-time hasbecome a critical problem. The keyword detection problem aims at searchingimportant information from massive text data to reflect the most importantevents or topics. However, social media data usually has unique features: thedocuments are usually short, the language is colloquial, and the data is likelyto have significant temporal patterns. Therefore, it could be challenging todiscover critical information from these text streams. In this paper, wepropose a novel method to address the keyword detection problem in socialmedia. Our model combines the Inverse Document Frequency (IDF) and LatentDirichlet Allocation (LDA) models to better cope with the distinct attributesof social media data, such as the number of likes, comments, and retweets. Byweighting the importance of each document based on these attributes, our methodcan effectively detect more representative keywords over time. Comprehensiveexperiments conducted under various conditions on Weibo data illustrate thatour approach outperforms the baselines in various evaluation metrics, includingprecision and recall for multiple problem settings.

Utilizing Social Media Attributes for Enhanced Keyword Detection: An IDF-LDA Model Applied to Sina Weibo