Call Me MayBe: Understanding Nature and Risks of Sharing Mobile Numbers on Online Social Networks

Prachi Jain, Paridhi Jain and Ponnurangam Kumaraguru

Little research explores the activity of sharing mobile num-
bers on OSNs, in particular via public posts. In this work,
we understand the characteristics and risks of mobile num-
bers 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 Face-
book 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 mo-
bile 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 num-
ber. 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.

Guest: Prof Ponnurangam Kumaraguru

Host: Prof Zvi Lotker, Ben-Gurion University.

Inferring User Interests from Tweet Times

Dinesh Ramasamy, Sriram Venkateswaran and Upamanyu Madhow


We propose and demonstrate the feasibility of a probabilistic framework for mining user interests from their tweet times alone, by exploiting the known timing of external events associated with these interests. This approach allows for making inferences on the interests of a large number of users for which text-based mining may become cumbersome, and also sidesteps the difficult problem of semantic/contextual analysis required for such text-based inferences. The statistic that we propose for gauging the user’s interest level is the probability that he/she tweets more frequently at certain times when this topic is in the “public eye” than at other times. We report on promising experimental results using Twitter data on detecting whether or not a user is a fan of a given baseball team, leveraging the known timing of games played by the team. Since people often interact with others who share similar interests, we extend our probabilistic frame- work to use the interest level estimates for other users with whom a person interacts (by referring to them in his/her tweets). We demonstrate that it is possible to significantly improve the detection probability (for a given false alarm rate) by such information pooling on the social graph.

Guest: Dinesh Ramasamy (UCSB)

Host:  Chen Avin