In this paper, we extensively study the impact of social signals (users' actions) obtained from several social networks on search ranking task. Social signals associated with web resources (documents) can be considered as an additional information that can play a vital role to estimate a priori importance of these resources. Particularly, we are interested in the freshness of signals and their diversity. We hypothesize that the moment (the date) when the user actions occur and the diversity of actions may impact the search performance. We propose to model these heterogeneous social features as document prior. We evaluate the effectiveness of our approach by carrying out extensive experiments on two different INEX datasets, namely SBS and IMDb, enriched with several social signals collected from social networks. Our experimental results consistently demonstrate the interest of integrating fresh and diverse signals in the retrieval process.

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.

This paper describes an approach of information retrieval which takes into account social signals associated with Web resources to estimate its relevance to a query. We show how these data, which are in the form of actions within social activities (e.g. Like, tweet), can be exploited to quantify social properties such as popularity and reputation. We propose a model that combines the social relevance, estimated from these properties, with the conventional textual relevance. We evaluated the effectiveness of our approach on IMDb dataset containing 32706 resources and their social characteristics collected from several social networks. We used also the selected criteria to learn models to determine their effectiveness in information retrieval. Our experimental results are promising and show the interest of integrating social signals in retrieval model to enhance a search.

Reviews on web resources (e.g. courses, movies) become increasingly exploited in text analysis tasks (e.g. opinion detection, controversy detection). This paper investigates contradiction intensity in reviews exploiting different features such as variation of ratings and variation of polarities around specific entities (e.g. aspects, topics). Firstly, aspects are identified according to the distributions of the emotional terms in the vicinity of the most frequent nouns in the reviews collection. Secondly, the polarity of each review segment containing an aspect is estimated. Only resources containing these aspects with opposite polarities are considered. Finally, some features are evaluated, using feature selection algorithms, to determine their impact on the effectiveness of contradiction intensity detection. The selected features are used to learn some state-of-the-art learning approaches. The experiments are conducted on the Massive Open Online Courses data set containing 2244 courses and their 73,873 reviews, collected from coursera.org. Results showed that variation of ratings, variation of polarities, and reviews quantity are the best predictors of contradiction intensity. Also, J48 was the most effective learning approach for this type of classification.

Social signals (users' actions) associated with web resources (documents) can be considered as an additional information that can play a role to estimate a priori importance of the resource. In this paper, we are particularly interested in: first, showing the impact of signals diversity associated to a resource on information retrieval performance; second, studying the influence of their social networks origin on their quality. We propose to model these social features as prior that we integrate into language model. We evaluated the effectiveness of our approach on IMDb dataset containing 167438 resources and their social signals collected from several social networks. Our experimental results are statistically significant and show the interest of integrating signals diversity in the retrieval process