6,952 research outputs found
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
Phantom cascades: The effect of hidden nodes on information diffusion
Research on information diffusion generally assumes complete knowledge of the
underlying network. However, in the presence of factors such as increasing
privacy awareness, restrictions on application programming interfaces (APIs)
and sampling strategies, this assumption rarely holds in the real world which
in turn leads to an underestimation of the size of information cascades. In
this work we study the effect of hidden network structure on information
diffusion processes. We characterise information cascades through activation
paths traversing visible and hidden parts of the network. We quantify diffusion
estimation error while varying the amount of hidden structure in five empirical
and synthetic network datasets and demonstrate the effect of topological
properties on this error. Finally, we suggest practical recommendations for
practitioners and propose a model to predict the cascade size with minimal
information regarding the underlying network.Comment: Preprint submitted to Elsevier Computer Communication
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
Modelling trust in semantic web applications
This paper examines some of the barriers to the adoption of car-sharing, termed carpooling in the US, and develops a framework for trusted recommendations. The framework is established on a semantic modelling approach putting forward its suitability to resolving adoption barriers while also highlighting the characteristics of trust that can be exploited. Identification is made of potential vocabularies, ontologies and public social networks which can be used as the basis for deriving direct and indirect trust values in an implementation
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
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