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Communication Dans Un Congrès Année : 2018

Stance Classification through Proximity-based Community Detection

Résumé

Numerous domains have interests in studying the viewpoints expressed online, be it for marketing, cybersecurity, or research purposes with the rise of computational social sciences. Current stance detection models are usually grounded on the specificities of some social platforms. This rigidity is unfortunate since it does not allow the integration of the multitude of signals informing effective stance detection. We propose the SCSD model, or Sequential Community-based Stance Detection model, a semi-supervised ensemble algorithm which considers these signals by modeling them as a multi-layer graph representing proximities between profiles. We use a handful of seed profiles, for whom we know the stance, to classify the rest of the profiles by exploiting like-minded communities. These communities represent profiles close enough to assume they share a similar stance on a given subject. Using datasets from two different social platforms, containing two to five stances, we show that by combining several types of proximity we can achieve excellent results. Moreover, we compare the proximities to find those which convey useful information in term of stance detection.
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Dates et versions

hal-03152874 , version 1 (25-02-2021)

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Ophélie Fraisier, Guillaume Cabanac, Yoann Pitarch, Romaric Besançon, Mohand Boughanem. Stance Classification through Proximity-based Community Detection. 29th ACM Conference on Hypertext and Social Media (HT 2018), Association for Computing Machinery, Jul 2018, Baltimore, MD, United States. pp.220-228, ⟨10.1145/3209542.3209549⟩. ⟨hal-03152874⟩
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