5,753 research outputs found
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
Crawling Facebook for Social Network Analysis Purposes
We describe our work in the collection and analysis of massive data describing the connections between participants to online social networks. Alternative approaches to social network data collection are defined and evaluated in practice, against the popular Facebook Web site. Thanks to our ad-hoc, privacy-compliant crawlers, two large samples, comprising millions of connections, have been collected; the data is anonymous and organized as an undirected graph. We describe a set of tools that we developed to analyze specific properties of such social-network graphs, i.e., among others, degree distribution, centrality measures, scaling laws and distribution of friendship.\u
A Survey of Location Prediction on Twitter
Locations, e.g., countries, states, cities, and point-of-interests, are
central to news, emergency events, and people's daily lives. Automatic
identification of locations associated with or mentioned in documents has been
explored for decades. As one of the most popular online social network
platforms, Twitter has attracted a large number of users who send millions of
tweets on daily basis. Due to the world-wide coverage of its users and
real-time freshness of tweets, location prediction on Twitter has gained
significant attention in recent years. Research efforts are spent on dealing
with new challenges and opportunities brought by the noisy, short, and
context-rich nature of tweets. In this survey, we aim at offering an overall
picture of location prediction on Twitter. Specifically, we concentrate on the
prediction of user home locations, tweet locations, and mentioned locations. We
first define the three tasks and review the evaluation metrics. By summarizing
Twitter network, tweet content, and tweet context as potential inputs, we then
structurally highlight how the problems depend on these inputs. Each dependency
is illustrated by a comprehensive review of the corresponding strategies
adopted in state-of-the-art approaches. In addition, we also briefly review two
related problems, i.e., semantic location prediction and point-of-interest
recommendation. Finally, we list future research directions.Comment: Accepted to TKDE. 30 pages, 1 figur
Towards Measuring Adversarial Twitter Interactions against Candidates in the US Midterm Elections
Adversarial interactions against politicians on social media such as Twitter
have significant impact on society. In particular they disrupt substantive
political discussions online, and may discourage people from seeking public
office. In this study, we measure the adversarial interactions against
candidates for the US House of Representatives during the run-up to the 2018 US
general election. We gather a new dataset consisting of 1.7 million tweets
involving candidates, one of the largest corpora focusing on political
discourse. We then develop a new technique for detecting tweets with toxic
content that are directed at any specific candidate.Such technique allows us to
more accurately quantify adversarial interactions towards political candidates.
Further, we introduce an algorithm to induce candidate-specific adversarial
terms to capture more nuanced adversarial interactions that previous techniques
may not consider toxic. Finally, we use these techniques to outline the breadth
of adversarial interactions seen in the election, including offensive
name-calling, threats of violence, posting discrediting information, attacks on
identity, and adversarial message repetition
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