Large Scale Retrieval of Social Network Pages by Interests of Their Followers

Mikhalkova, Elena; Karyakin, Yuri; Glukhikh, I. I.

doi:10.1007/978-3-319-93698-7_18

Cited by 2 publications

(1 citation statement)

References 34 publications

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“…Так, в работах [34][35][36] рассматриваются подходы к автоматической классификации постов в Twitter с помощью словарей. В статьях [37][38][39] описываются результаты применения методов машинного обучения к задаче классификации текстов социальных сетей. В работах [40,41] оценивается эффективность использования признаков различной природы для классификации постов.…”

Section: обзор смежных работunclassified

Detecting Mentions of Green Practices in Social Media Based on Text Classification

Glazkova

Zakharova

Zakharov

et al. 2022

Model. anal. inf. sist.

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The paper is devoted to the task of searching for mentions of green practices in social media texts. The relevance of this task is dictated by the need to expand existing knowledge about the use of green practices in society and the spread of existing green practices. This paper uses a text corpus consisting of the texts published on the environmental communities of the VKontakte social network. The corpus is equipped with an expert markup of the mention of nine types of green practices. As part of this work, a semi-automatic approach is proposed to the collection of additional texts to reduce the class imbalance in the corpus. The approach includes the following steps: detecting the most frequent words for each practice type; automatic collecting texts in social media that contain the detected frequent words; expert verification and filtering of collected texts. The four machine learning models are compared to find the mentions of green practices on the two variants of the corpus: original and augmented using the proposed approach. Among the listed models, the highest averaged F1-score (81.32%) was achieved by Conversational RuBERT fine-tuned on the augmented corpus. Conversational RuBERT model was chosen for the implementation of the application prototype. The main function of the prototype is to detect the presence of the mention of nine types of green practices in the text. The prototype is implemented in the form of the Telegram chatbot.

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Section: обзор смежных работunclassified

Detecting Mentions of Green Practices in Social Media Based on Text Classification

Glazkova

Zakharova

Zakharov

et al. 2022

Model. anal. inf. sist.

View full text Add to dashboard Cite

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Large Scale Retrieval of Social Network Pages by Interests of Their Followers

Mikhalkova¹,

Karyakin²,

Glukhikh³

2018

Lecture Notes in Computer Science

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Being a matter of cognition, user interests should be apt to classification independent of the language of users, social network and content of interest itself. To prove it, we analyze a collection of English and Russian Twitter and Vkontakte community pages by interests of their followers. First, we create a model of Major Interests (MaIs) with the help of expert analysis and then classify a set of pages using machine learning algorithms (SVM, Neural Network, Naive Bayes, and some other). We take three interest domains that are typical of both English and Russian-speaking communities: football, rock music, vegetarianism. The results of classification show a greater correlation between Russian-Vkontakte and Russian-Twitter pages while English-Twitter pages appear to provide the highest score.

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Large Scale Retrieval of Social Network Pages by Interests of Their Followers

Cited by 2 publications

References 34 publications

Detecting Mentions of Green Practices in Social Media Based on Text Classification

Detecting Mentions of Green Practices in Social Media Based on Text Classification

Large Scale Retrieval of Social Network Pages by Interests of Their Followers

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