Tracking the History and Evolution of Entities: Entity-centric Temporal Analysis of Large Social Media Archives

How did the popularity of the Greek Prime Minister evolve in 2015? How did the predominant sentiment about him vary during that period? Were there any controversial sub-periods? What other entities were related to him during these periods? To answer these questions, one needs to analyze archived documents and data about the query entities, such as old news articles or social media archives. In particular, user-generated content posted in social networks, like Twitter and Facebook, can be seen as a comprehensive documentation of our society, and thus meaningful analysis methods over such archived data are of immense value for sociologists, historians and other interested parties who want to study the history and evolution of entities and events. To this end, in this paper we propose an entity-centric approach to analyze social media archives and we define measures that allow studying how entities were reflected in social media in different time periods and under different aspects, like popularity, attitude, controversiality, and connectedness with other entities. A case study using a large Twitter archive of four years illustrates the insights that can be gained by such an entity-centric and multi-aspect analysis.Comment: This is a preprint of an article accepted for publication in the International Journal on Digital Libraries (2018

MinoanER: Schema-Agnostic, Non-Iterative, Massively Parallel Resolution of Web Entities

Entity Resolution (ER) aims to identify different descriptions in various Knowledge Bases (KBs) that refer to the same entity. ER is challenged by the Variety, Volume and Veracity of entity descriptions published in the Web of Data. To address them, we propose the MinoanER framework that simultaneously fulfills full automation, support of highly heterogeneous entities, and massive parallelization of the ER process. MinoanER leverages a token-based similarity of entities to define a new metric that derives the similarity of neighboring entities from the most important relations, as they are indicated only by statistics. A composite blocking method is employed to capture different sources of matching evidence from the content, neighbors, or names of entities. The search space of candidate pairs for comparison is compactly abstracted by a novel disjunctive blocking graph and processed by a non-iterative, massively parallel matching algorithm that consists of four generic, schema-agnostic matching rules that are quite robust with respect to their internal configuration. We demonstrate that the effectiveness of MinoanER is comparable to existing ER tools over real KBs exhibiting low Variety, but it outperforms them significantly when matching KBs with high Variety.Comment: Presented at EDBT 2001

F2VAE : A Framework for Mitigating User Unfairness in Recommendation Systems

Recommendation algorithms are widely used nowadays, especially in scenarios of information overload (i.e., when users have too many options to choose from), due to their ability to suggest potentially relevant items to users in a personalized fashion. Users, nevertheless, might be considered as separated in groups according to sensitive attributes, such as age, gender or nationality, and the recommendation process might be biased towards one of these groups. If observed, this bias has to be mitigated actively, or it can propagate and be amplified over time. Here, we consider a relevant difference of recommendation quality among groups as unfair, and we argue that this difference should be maintained as low as possible. We propose a framework named F2VAE for mitigating user-oriented unfairness in recommender systems. The framework is based on Variational Autoencoders (VAE) and it introduces two extra terms in VAE's standard loss function, one associated to fair representation and another one associated to fair recommendation. The conflicting objectives associated to these terms are discussed in details in a series of experiments considering the bias associated to the users' nationality in a music consumption dataset. We recall recent works proposed for generating fair representations in the context of classification, and we adapt one of these methods to the recommendation task. F2VAE was able to increase the precision by approximately 1% while reducing the unfairness by 21% when compared to standard VAE.acceptedVersionPeer reviewe