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Emotional Social Signals for Search Ranking

Ismail Badache 1, 2 Mohand Boughanem 2
1 R2I - Recherche d’information et Interactions
LIS - Laboratoire d'Informatique et Systèmes
2 IRIT-IRIS - Recherche d’Information et Synthèse d’Information
IRIT - Institut de recherche en informatique de Toulouse
Abstract : A large amount of social feedback expressed by social signals (e.g. like, +1, rating) are assigned to web resources. These signals are often exploited as additional sources of evidence in search engines. Our objective in this paper is to study the impact of the new social signals, called Facebook reactions (love, haha, angry, wow, sad) in the retrieval. These reactions allow users to express more nuanced emotions compared to classic signals (e.g. like, share). First, we analyze these reactions and show how users use these signals to interact with posts. Second, we evaluate the impact of each such reaction in the retrieval, by comparing them to both the textual model without social features and the first classical signal (like-based model). These social features are modeled as document prior and are integrated into a language model. We conducted a series of experiments on IMDb dataset. Our findings reveal that incorporating social features is a promising approach for improving the retrieval ranking performance.
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Submitted on : Thursday, September 13, 2018 - 3:40:20 PM
Last modification on : Sunday, June 21, 2020 - 6:00:16 PM

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Ismail Badache, Mohand Boughanem. Emotional Social Signals for Search Ranking. 40th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR 2017), Aug 2017, Tokyo, Japan. pp. 1053-1056, ⟨10.1145/3077136.3080718⟩. ⟨hal-01873739⟩

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