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Survey of Generative Methods for Social Media Analysis

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arxiv 2112.07041 v1 pith:JTN5K25Y submitted 2021-12-13 cs.SI cs.LG

classification cs.SIcs.LG
keywords socialimportantmediasurveyanalysisdynamicsgenerativemethods
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This survey draws a broad-stroke, panoramic picture of the State of the Art (SoTA) of the research in generative methods for the analysis of social media data. It fills a void, as the existing survey articles are either much narrower in their scope or are dated. We included two important aspects that currently gain importance in mining and modeling social media: dynamics and networks. Social dynamics are important for understanding the spreading of influence or diseases, formation of friendships, the productivity of teams, etc. Networks, on the other hand, may capture various complex relationships providing additional insight and identifying important patterns that would otherwise go unnoticed.

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Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. SGN: A Similarity-based Generative Network for Data Generation under Distribution Shift

    cs.LG 2026-07 conditional novelty 4.0 of 10

    SGN generates target-domain data by decoding linear mixes of encoded target examples in a label-similarity-structured latent space, without updating the source-trained model.

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