Paridhi Jain

@i seek 'fb.me': identifying users across multiple online social networks

An online user joins multiple social networks in order to enjoy different services. On each joined social network, she creates an identity and constitutes its three major dimensions namely profile, content and connection network. She largely governs her identity formulation on any social network and therefore can manipulate multiple aspects of it. With no global identifier to mark her presence uniquely in the online domain, her online identities remain unlinked, isolated and difficult to search. Literature has proposed identity search methods on the basis of profile attributes, but has left the other identity dimensions e.g. content and network, unexplored. In this work, we introduce two novel identity search algorithms based on content and network attributes and improve on traditional identity search algorithm based on profile attributes of a user. We apply proposed identity search algorithms to find a users identity on Facebook, given her identity on Twitter. We report that a combination of proposed identity search algorithms found Facebook identity for 39% of Twitter users searched while traditional method based on profile attributes found Facebook identity for only 27.4%. Each proposed identity search algorithm access publicly accessible attributes of a user on any social network. We deploy an identity resolution system, Finding Nemo, which uses proposed identity search methods to find a Twitter users identity on Facebook. We conclude that inclusion of more than one identity search algorithm, each exploiting distinct dimensional attributes of an identity, helps in improving the accuracy of an identity resolution process.

Cross-Pollination of Information in Online Social Media: A Case Study on Popular Social Networks

Owing to the popularity of Online Social Media (OSM), Internet users share a lot of information on and across OSM services every day. Users recommend, comment, and forward information they receive from friends, contributing in spreading the information in and across OSM services. We term this information diffusion process from one OSM service to another as Cross-Pollination, and the network formed by users who participate in Cross-Pollination and content produced in the network as Cross-Pollinated network. Research has been done about information diffusion within one OSM service, but little is known about Cross-Pollination. We aim at filling this gap by studying how information from three popular OSM services (You Tube, Flickr and Foursquare) diffuses on Twitter, the most popular microblogging service. Our results show that Cross-Pollinated networks follow temporal and topological characteristics of Twitter. Furthermore, popularity of information on source OSM (You Tube, Flickr and Foursquare) does not imply its popularity on Twitter.

Call me maybe: understanding nature and risks of sharing mobile numbers on online social networks

Little research explores the activity of sharing mobile numbers on OSNs, in particular via public posts. In this work, we understand the characteristics and risks of mobile numbers shared on OSNs either via profile or public posts and focus on Indian mobile numbers. We collected 76,347 unique mobile numbers posted by 85,905 users on Twitter and Facebook and analyzed 2,997 numbers, prefixed with +91. We observed that most users shared their own mobile numbers to spread urgent information and to market products, IT facilities and escort business. Users resorted to applications like Twitterfeed and TweetDeck to post and popularize mobile numbers on multiple OSNs. To assess risks associated with mobile numbers exposed on OSNs, we used mobile numbers to gain sensitive information (e.g. name, Voter ID) about their owners. We communicated the observed risks to the owners by calling them on their mobile number. Few users were surprised to know the online presence of their number, while few users intentionally put it online for business purposes. With these observations, we highlight that there is a need to monitor leakage of mobile numbers via profile and public posts. To the best of our knowledge, this is the first exploratory study to critically investigate the exposure of mobile numbers on OSNs.

On the Dynamics of Username Changing Behavior on Twitter

People extensively use username to lookup users, their profiles and tweets that mention them via Twitter search engine. Often, the searched username is outdated due to a recent username change and no longer refers to the user of interest. Search by the users old username results in a failed attempt to reach the users profile, thereby making others falsely believe that the user account has been deactivated. Such search can also redirect to a different user who later picks the old username, thereby reaching to a different person altogether. Past studies show that a substantial section of Twitter users change their username over time. We also observe similar trends when tracked 8.7 million users on Twitter for a duration of two months. To this point, little is known about how and why do these users undergo changes to their username, given the consequences of unreachability. To answer this, we analyze username changing behavior of carefully selected users on Twitter and find that users change username frequently within short time intervals (a day) and choose new username un-related to the old one. Few favor a username by repeatedly choosing it multiple times. We explore few of the many reasons that may have caused username changes. We believe that studying username changing behavior can help correctly find the user of interest in addition to learning username creation strategies and uncovering plausible malicious intentions for the username change.

Nudging Nemo: Helping Users Control Linkability Across Social Networks

The last decade has witnessed a boom in social networking platforms; each new platform is unique in its own ways, and offers a different set of features and services. In order to avail these services, users end up creating multiple virtual identities across these platforms. Researchers have proposed numerous techniques to resolve multiple such identities of a user across different platforms. However, the ability to link different identities poses a threat to the users’ privacy; users may or may not want their identities to be linkable across networks. In this paper, we propose Nudging Nemo, a framework which assists users to control the linkability of their identities across multiple platforms. We model the notion of linkability as the probability of an adversary (who is part of the user’s network) being able to link two profiles across different platforms, to the same real user. Nudging Nemo has two components; a linkability calculator which uses state-of-the-art identity resolution techniques to compute a normalized linkability measure for each pair of social network platforms used by a user, and a soft paternalistic nudge, which alerts the user if any of their activity violates their preferred linkability. We evaluate the effectiveness of the nudge by conducting a controlled user study on privacy conscious users who maintain their accounts on Facebook, Twitter, and Instagram. Outcomes of user study confirmed that the proposed framework helped most of the participants to take informed decisions, thereby preventing inadvertent exposure of their personal information across social network services.

EnTwine: Feature Analysis and Candidate Selection for Social User Identity Aggregation

Organizations measure their social audience based on the number of users, fans, and followers on social media. Every social media platform has its user identity and a single user is present across varied platforms. Due to the disconnected user profiles, identifying duplicate users across media is non-trivial. There is a need to create a complete view of a user for various applications such as targeting and user profile construction. This view is not easily available due to the individual identities. In this work, we explore the feature space across social media that can be leveraged for intelligent user identity aggregation. Further, we present a two-phased unified identity creation process using our feature analysis, unsupervised candidate selection, and supervised user matching algorithms on four different social networks.