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 has
become a critical problem. The keyword detection problem aims at searching
important information from massive text data to reflect the most important
events or topics. However, social media data usually has unique features: the
documents are usually short, the language is colloquial, and the data is likely
to have significant temporal patterns. Therefore, it could be challenging to
discover critical information from these text streams. In this paper, we
propose a novel method to address the keyword detection problem in social
media. Our model combines the Inverse Document Frequency (IDF) and Latent
Dirichlet Allocation (LDA) models to better cope with the distinct attributes
of social media data, such as the number of likes, comments, and retweets. By
weighting the importance of each document based on these attributes, our method
can effectively detect more representative keywords over time. Comprehensive
experiments conducted under various conditions on Weibo data illustrate that
our approach outperforms the baselines in various evaluation metrics, including
precision and recall for multiple problem settings.

本文提出了一种基于 IDF 和 LDA 模型相结合的方法，以更好地应对社交媒体数据的不同属性，通过基于点赞数、评论数和转发数等属性来加权每个文档的重要性，从而有效地检测出越来越具有代表性的关键词，实验证明该方法在各种评估指标，包括多个问题设置的精度和召回率等方面优于基准。

基于社交媒体属性的关键词检测：Sina 微博上的 IDF-LDA 模型应用

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

The COVID-19 pandemic has been accompanied by an `infodemic' -- of accurate
and inaccurate health information across social media. Detecting misinformation
amidst dynamically changing information landscape is challenging; identifying
relevant keywords and posts is arduous due to the large amount of human effort
required to inspect the content and sources of posts. We aim to reduce the
resource cost of this process by introducing a weakly-supervised iterative
graph-based approach to detect keywords, topics, and themes related to
misinformation, with a focus on COVID-19. Our approach can successfully detect
specific topics from general misinformation-related seed words in a few seed
texts. Our approach utilizes the BERT-based Word Graph Search (BWGS) algorithm
that builds on context-based neural network embeddings for retrieving
misinformation-related posts. We utilize Latent Dirichlet Allocation (LDA)
topic modeling for obtaining misinformation-related themes from the texts
returned by BWGS. Furthermore, we propose the BERT-based Multi-directional Word
Graph Search (BMDWGS) algorithm that utilizes greater starting context
information for misinformation extraction. In addition to a qualitative
analysis of our approach, our quantitative analyses show that BWGS and BMDWGS
are effective in extracting misinformation-related content compared to common
baselines in low data resource settings. Extracting such content is useful for
uncovering prevalent misconceptions and concerns and for facilitating precision
public health messaging campaigns to improve health behaviors.

本文提出了一种基于图的弱监督迭代方法来检测与 COVID-19 相关的谣言、主题和观点，利用 Bert-based Word Graph Search 进行误信息提取，具有较好的准确性，可用于改善公共卫生信息传播。