29,866 research outputs found
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
Social Relations and Methods in Recommender Systems: A Systematic Review
With the constant growth of information, data sparsity problems, and cold start have become a complex problem in obtaining accurate recommendations. Currently, authors consider the user's historical behavior and find contextual information about the user, such as social relationships, time information, and location. In this work, a systematic review of the literature on recommender systems that use the information on social relationships between users was carried out. As the main findings, social relations were classified into three groups: trust, friend activities, and user interactions. Likewise, the collaborative filtering approach was the most used, and with the best results, considering the methods based on memory and model. The most used metrics that we found, and the recommendation methods studied in mobile applications are presented. The information provided by this study can be valuable to increase the precision of the recommendations
Potential mass surveillance and privacy violations in proximity-based social applications
Proximity-based social applications let users interact with people that are
currently close to them, by revealing some information about their preferences
and whereabouts. This information is acquired through passive geo-localisation
and used to build a sense of serendipitous discovery of people, places and
interests. Unfortunately, while this class of applications opens different
interactions possibilities for people in urban settings, obtaining access to
certain identity information could lead a possible privacy attacker to identify
and follow a user in their movements in a specific period of time. The same
information shared through the platform could also help an attacker to link the
victim's online profiles to physical identities. We analyse a set of popular
dating application that shares users relative distances within a certain radius
and show how, by using the information shared on these platforms, it is
possible to formalise a multilateration attack, able to identify the user
actual position. The same attack can also be used to follow a user in all their
movements within a certain period of time, therefore identifying their habits
and Points of Interest across the city. Furthermore we introduce a social
attack which uses common Facebook likes to profile a person and finally
identify their real identity
Contextual Attention Recurrent Architecture for Context-aware Venue Recommendation
Venue recommendation systems aim to effectively rank a list of interesting venues users should visit based on their historical feedback (e.g. checkins). Such systems are increasingly deployed by Location-based Social Networks (LBSNs) such as Foursquare and Yelp to enhance their usefulness to users. Recently, various RNN architectures have been proposed to incorporate contextual information associated with the users' sequence of checkins (e.g. time of the day, location of venues) to effectively capture the users' dynamic preferences. However, these architectures assume that different types of contexts have an identical impact on the users' preferences, which may not hold in practice. For example, an ordinary context such as the time of the day reflects the user's current contextual preferences, whereas a transition context - such as a time interval from their last visited venue - indicates a transition effect from past behaviour to future behaviour. To address these challenges, we propose a novel Contextual Attention Recurrent Architecture (CARA) that leverages both sequences of feedback and contextual information associated with the sequences to capture the users' dynamic preferences. Our proposed recurrent architecture consists of two types of gating mechanisms, namely 1) a contextual attention gate that controls the influence of the ordinary context on the users' contextual preferences and 2) a time- and geo-based gate that controls the influence of the hidden state from the previous checkin based on the transition context. Thorough experiments on three large checkin and rating datasets from commercial LBSNs demonstrate the effectiveness of our proposed CARA architecture by significantly outperforming many state-of-the-art RNN architectures and factorisation approaches
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