A pipeline combining LDA, Top2Vec, GPT-2, similarity analysis, and human evaluation extracts policy agendas from social media with reported good inter-rater agreement and cosine similarity scores.
Characterization of citizens using word2vec and latent topic analysis in a large set of tweets
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abstract
With the increasing use of the Internet and mobile devices, social networks are becoming the most used media to communicate citizens' ideas and thoughts. This information is very useful to identify communities with common ideas based on what they publish in the network. This paper presents a method to automatically detect city communities based on machine learning techniques applied to a set of tweets from Bogot\'a's citizens. An analysis was performed in a collection of 2,634,176 tweets gathered from Twitter in a period of six months. Results show that the proposed method is an interesting tool to characterize a city population based on a machine learning methods and text analytics.
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cs.CY 1years
2026 1verdicts
UNVERDICTED 1representative citing papers
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Using machine learning to build public policy agenda from social media conversations
A pipeline combining LDA, Top2Vec, GPT-2, similarity analysis, and human evaluation extracts policy agendas from social media with reported good inter-rater agreement and cosine similarity scores.