Tracking News Stories in Short Messages in the Era of Infodemic

Abstract

Tracking news stories in documents is a way to deal with the large amount of information that surrounds us everyday, to reduce the noise and to detect emergent topics in news. Since the Covid-19 outbreak, the world has known a new problem: infodemic. News article titles are massively shared on social networks and the analysis of trends and growing topics is complex. Grouping documents in news stories lowers the number of topics to analyse and the information to ingest and/or evaluate. Our study proposes to analyse news tracking with little information provided by titles on social networks. In this paper, we take advantage of datasets of public news article titles to experiment news tracking algorithms on short messages. We evaluate the clustering performance with little amount of data per document. We deal with the document representation (sparse with TF-IDF and dense using Transformers [26]), its impact on the results and why it is key to this type of work. We used a supervised algorithm proposed by Miranda et al. [22] and K-Means to provide evaluations for different use cases. We found that TF-IDF vectors are not always the best ones to group documents, and that algorithms are sensitive to the type of representation. Knowing this, we recommend taking both aspects into account while tracking news stories in short messages. With this paper, we share all the source code and resources we handled.

Partial Mobilization: Tracking Multilingual Information Flows amongst Russian Media Outlets and Telegram

“…We compare the semantic content of our embedded messages and paragraphs utilizing cosine similarity (Song et al 2020). As found in previous works (Hanley, Kumar, and Durumeric 2023b;Song et al 2020;Bernard et al 2022), a cosine similarity threshold between 0.60 and 0.80 can be utilized to determine whether two pieces of text are about the same topic. For instance, with the same model, Phan et al (2022), found that a threshold near 0.715 achieved the best results.…”