Topic Modeling in Russia: Current Approaches and Issues in Methodology

Bodrunova, Svetlana S.

doi:10.1007/978-3-030-42855-6_23

Cited by 2 publications

(1 citation statement)

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“…In particular, topicality extraction is a task that most often employs probabilistic clustering such as topic modeling [1][2][3], its extensions, or combinations with sentiment analysis [4] or other classes of tasks. However, topic modeling, despite its wide area of application, has many well-known disadvantages.…”

Section: Introductionmentioning

confidence: 99%

Transformer-Based Abstractive Summarization for Reddit and Twitter: Single Posts vs. Comment Pools in Three Languages

2022

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Abstractive summarization is a technique that allows for extracting condensed meanings from long texts, with a variety of potential practical applications. Nonetheless, today’s abstractive summarization research is limited to testing the models on various types of data, which brings only marginal improvements and does not lead to massive practical employment of the method. In particular, abstractive summarization is not used for social media research, where it would be very useful for opinion and topic mining due to the complications that social media data create for other methods of textual analysis. Of all social media, Reddit is most frequently used for testing new neural models of text summarization on large-scale datasets in English, without further testing on real-world smaller-size data in various languages or from various other platforms. Moreover, for social media, summarizing pools of texts (one-author posts, comment threads, discussion cascades, etc.) may bring crucial results relevant for social studies, which have not yet been tested. However, the existing methods of abstractive summarization are not fine-tuned for social media data and have next-to-never been applied to data from platforms beyond Reddit, nor for comments or non-English user texts. We address these research gaps by fine-tuning the newest Transformer-based neural network models LongFormer and T5 and testing them against BART, and on real-world data from Reddit, with improvements of up to 2%. Then, we apply the best model (fine-tuned T5) to pools of comments from Reddit and assess the similarity of post and comment summarizations. Further, to overcome the 500-token limitation of T5 for analyzing social media pools that are usually bigger, we apply LongFormer Large and T5 Large to pools of tweets from a large-scale discussion on the Charlie Hebdo massacre in three languages and prove that pool summarizations may be used for detecting micro-shifts in agendas of networked discussions. Our results show, however, that additional learning is definitely needed for German and French, as the results for these languages are non-satisfactory, and more fine-tuning is needed even in English for Twitter data. Thus, we show that a ‘one-for-all’ neural-network summarization model is still impossible to reach, while fine-tuning for platform affordances works well. We also show that fine-tuned T5 works best for small-scale social media data, but LongFormer is helpful for larger-scale pool summarizations.

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Section: Introductionmentioning

confidence: 99%

Transformer-Based Abstractive Summarization for Reddit and Twitter: Single Posts vs. Comment Pools in Three Languages

2022

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Beyond Topic Modeling: Analyzing Historical Text with Large Language Models

Kuznetsov

2024

Историческая Информатика

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The article explores the potential of large language models in thematic analysis of historical texts, exemplified by the 1849 diary of Vologda gymnasium student Kirill Antonovich Berezkin. This rich source illuminates the everyday life, worldview, and social interactions of a young individual in mid-19th century provincial Russia. The diary offers a multifaceted narrative, capturing cultural events, political contexts, and personal introspections. By meticulously analyzing this text, researchers can reconstruct not just an individual's experiences, but also gain profound insights into the social, cultural, and educational landscape of the era. Employing the Gemini 1.5 Pro model, renowned for processing extensive textual data, the study conducted a comprehensive analysis. The research methodology involved examining the diary both holistically and through monthly segmentation, enabling the identification of nuanced content aspects. The novelty of the approach lies in applying modern large language models to a Russian historical document. The results demonstrated the model's remarkable capability to identify key themes, successfully isolating eight major thematic areas that reflect the gymnasium student's life. Utilizing parallel prompting with a monthly text breakdown revealed specific themes and subtleties that a comprehensive review might have overlooked. The study ultimately validates the effectiveness of large language models in historical source analysis, presenting promising opportunities for automating topic modeling and uncovering hidden patterns in extensive textual datasets. However, the inherently stochastic nature of these models necessitates multiple analyses, careful result interpretation, and critical comparison with traditional historical research methodologies.

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Topic Modeling in Russia: Current Approaches and Issues in Methodology

Cited by 2 publications

References 45 publications

Transformer-Based Abstractive Summarization for Reddit and Twitter: Single Posts vs. Comment Pools in Three Languages

Transformer-Based Abstractive Summarization for Reddit and Twitter: Single Posts vs. Comment Pools in Three Languages

Beyond Topic Modeling: Analyzing Historical Text with Large Language Models

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