Lamjed Ben Jabeur

Active microbloggers: identifying influencers, leaders and discussers in microblogging networks

This paper presents a social approach for identifying key actors in microblogging social network. In particular, we propose three specific link analysis algorithms called InfRank, LeadRank and DiscussRank that identify influencers, leaders and discussers, respectively. Conducted experiments on TREC 2011 Microblog dataset, show that the proposed algorithms outperform close microblogger ranking approaches.

Social Media-Based Collaborative Information Access: Analysis of Online Crisis-Related Twitter Conversations

The notion of implicit (or explicit) collaborative information access refers to systems and practices allowing a group of users to unintentionally (respectively intentionally) seek, share and retrieve information to achieve similar (respectively shared) information-related goals. Despite an increasing adoption in social environments, collaboration behavior in information seeking and retrieval is mainly limited to small-sized groups, generally restricted to working spaces. Much remains to be learned about collaborative information seeking within open web social spaces. This paper is an attempt to better understand either implicit or explicit collaboration by studying Twitter, one of the most popular and widely used social networks. We study in particular the complex intertwinement of human interactions induced by both collaboration and social networking. We empirically explore explicit collaborative interactions based on focused conversation streams during two crisis. We identify structural patterns of temporally representative conversation subgraphs and represent their topics using Latent Dirichlet Allocation (LDA) modeling. Our main findings suggest that: 1) the critical mass of collaboration is generally limited to small-sized flat networks, with or without an influential user, 2) users are active as members of weakly overlapping groups and engage in numerous collaborative search and sharing tasks dealing with different topics, and 3) collaborative group ties evolve within the time-span of conversations.

On ranking relevant entities in heterogeneous networks using a language‐based model

A new challenge, accessing multiple relevant entities, arises from the availability of linked heterogeneous data. In this article, we address more specifically the problem of accessing relevant entities, such as publications and authors within a bibliographic network, given an information need. We propose a novel algorithm, called BibRank, that estimates a joint relevance of documents and authors within a bibliographic network. This model ranks each type of entity using a score propagation algorithm with respect to the query topic and the structure of the underlying bi-type information entity network. Evidence sources, namely content-based and network-based scores, are both used to estimate the topical similarity between connected entities. For this purpose, authorship relationships are analyzed through a language model-based score on the one hand and on the other hand, non topically related entities of the same type are detected through marginal citations. The article reports the results of experiments using the Bibrank algorithm for an information retrieval task. The CiteSeerX bibliographic data set forms the basis for the topical query automatic generation and evaluation. We show that a statistically significant improvement over closely related ranking models is achieved.

A Product Feature-Based User-Centric Ranking Model for E-Commerce Search

During the online shopping process, users search for interesting products in order to quickly access those that fit with their needs among a long tail of similar or closely related products. Our contribution addresses head queries that are frequently submitted on e-commerce Web sites. Head queries usually target featured products with several variations , accessories, and complementary products. We present in this paper a product feature-based user-centric model for product search involving, in addition to product characteristics, the user engagement toward the product. This model has been evaluated through the product search track of the LL4IR lab at CLEF 2015 in order to highlight the effectiveness of our model as well as the impact of the user engagement factor.

On Using Social Context to Model Information Retrieval and Collaboration in Scientific Research Community

In this chapter, we particularly address the shift in the usage from the personal context toward social context in information retrieval area. We are specifically interested in the scientific communities and related practices for information retrieval, seeking, and collaboration. Therefore, we present an overview of works that tackle the problem of information retrieval in scientific community from a social perspective. Regarding the objective of exploiting the sociometric foundations in the context of literature retrieval, we present our social retrieval model. We particularly model the author’s importance within the community, formalize the degree of collaboration between authors, taggers’ interest in a scientific topic, and combine them to tune information relevance in response to a user query. Experimental evaluation using a scientific corpus of documents and social data extracted from the academic social network CiteULike (http://www.citeulike.org) is presented and shows the impact of the social view on the retrieval effectiveness assuming different assumptions of relevance emerging from socially endorsed data.