90,214 research outputs found

    Similarity-based user identification across social networks

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    Σε αυτή τη διπλωματική μελετάμε την ταυτοποίηση των χρηστών στα κοινωνικά δίκτυα, εκπαιδεύοντας εναν συνδυασμό διαφορετικών μετρικών ομοιότητας. Αυτή η εφαρμογή γίνεται ιδιαίτερα ενδιαφέρουσα, καθώς η αύξηση του αριθμού και της ποικιλομορφίας των κοινωνικών δικτύων και η παρουσία των ατόμων σε πολλαπλά δίκτυα γίνεται πλέον κοινός τόπος. Εχοντας ως κίνητρο την ανάγκη να επαλήθευσουμε τις πληροφορίες που εμφανίζονται σε κοινωνικά δίκτυα, όπως μελετάται στο ερευνητικό πρόγραμμα REVEAL (REVEALing hidden concepts in Social Media), η παρουσία ατόμων σε διαφορετικά δίκτυα παρέχει μια ενδιαφέρουσα ευκαιρία : μπορούμε να χρησιμοποιήσουμε τις πληροφορίες από ένα δίκτυο για να επαληθεύσουμε τις πληροφορίες που εμφανίζονται σε ένα άλλο. Για να επιτευχθεί αυτό, χρειάζεται να ταυτοποιήσουμε τους χρήστες σε διαφορετικά δίκτυα. Προσεγγίζουμε αυτό το πρόβλημα συνδυάζοντας κάποια μέτρα ομοιότητας που λαμβάνουν υπόψη τον εργασιακό χώρο, την τοποθεσία, τα επαγγελματικά ενδιαφέροντα και εμπειρία των χρηστών, όπως αναφέρονται και καθορίζονται στα διάφορα δίκτυα. Εχουμε πειραματιστεί με μια ποικιλία από συνδυαστικές προσεγγίσεις, που κυμαίνονται από την απλή κατά μέσο όρο ταξινόμηση έως υβριδικούς εκπαιδευόμενους ταξινομητές. Τα πειράματά μας δείχνουν ότι, υπό ορισμένες προϋποθέσεις, η ταυτοποίηση χρηστών είναι δυνατή με αρκετά υψηλή ακρίβεια για να επιτευχθεί ο στόχος της επαλήθευσης των πληροφοριών.In this thesis we study the identifiability of users across social networks, with a trainable combination of different similarity metrics. This application is becoming particularly interesting as the number and variety of social networks increase and the presence of individuals in multiple networks is becoming commonplace. Motivated by the need to verify information that appears in social networks, as addressed by the research project REVEAL (REVEALing hidden concepts in Social Media), the presence of individuals in different networks provides an interesting opportunity: we can use information from one network to verify information that appears in another. In order to achieve this, we need to identify users across networks. We approach this problem by a combination of similarity measures that take into account the users’ affiliation, location, professional interests and past experience, as stated in the different networks. We experimented with a variety of combination approaches, ranging from simple averaging to trained hybrid models. Our experiments show that, under certain conditions, identification is possible with sufficiently high accuracy to support the goal of verification

    Understanding the user display names across social networks

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    The display names that an individual uses in various online social networks always contain some redundant information because some people tend to use the similar names across different networks to make them easier to remember or to build their online reputation. In this paper, we aim to measure the redundant information between different display names of the same individual. Based on the cross-site linking function, we first develop a specific distributed crawler to extract the display names that individuals select for different social networks, and we give an overview on the display names we extracted. Then we measure and analyze the redundant information in three ways: length similarity, character similarity and letter distribution similarity, comparing with display names of different individuals. We also analyze the evolution of redundant information over time. We find 45% of users tend to use the same display name across OSNs. Our findings also demonstrate that display names of the same individual show high similarity. The evolution analysis results show that redundant information is time-independent. Awareness of the redundant information between the display names can benefit many applications, such as user identification across social networks

    A deep dive into user display names across social networks

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    The display names from an individual across Online Social Networks (OSNs) always contain abundant information redundancies because most users tend to use one main name or similar names across OSNs to make them easier to remember or to build their online reputation. These information redundancies are of great benefit to information fusion across OSNs. In this paper, we aim to measure these information redundancies between different display names of the same individual. Based on the cross-site linking function of Foursquare, we first develop a distributed crawler to extract the display names that individuals used in Facebook, Twitter and Foursquare, respectively. We construct three display name datasets across three OSNs, and measure the information redundancies in three ways: length similarity, character similarity and letter distribution similarity. We also analyze the evolution of redundant information over time. Finally, we apply the measurement results to the user identification across OSNs. We find that (1) more than 45% of users tend to use the same display name across OSNs; (2) the display names of the same individual for different OSNs show high similarity; (3) the information redundancies of display names are time-independent; (4) the AUC values of user identification results only based on display names are more than 0.9 on three datasets

    #mytweet via Instagram: Exploring User Behaviour across Multiple Social Networks

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    We study how users of multiple online social networks (OSNs) employ and share information by studying a common user pool that use six OSNs - Flickr, Google+, Instagram, Tumblr, Twitter, and YouTube. We analyze the temporal and topical signature of users' sharing behaviour, showing how they exhibit distinct behaviorial patterns on different networks. We also examine cross-sharing (i.e., the act of user broadcasting their activity to multiple OSNs near-simultaneously), a previously-unstudied behaviour and demonstrate how certain OSNs play the roles of originating source and destination sinks.Comment: IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, 2015. This is the pre-peer reviewed version and the final version is available at http://wing.comp.nus.edu.sg/publications/2015/lim-et-al-15.pd
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