96 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
Beyond Trending Topics: Real-World Event Identification on Twitter
User-contributed messages on social media sites such as Twitter have emerged as powerful, real-time means of information sharing on the Web. These short messages tend to reflect a variety of events in real time, earlier than other social media sites such as Flickr or YouTube, making Twitter particularly well suited as a source of real-time event content. In this paper, we explore approaches for analyzing the stream of Twitter messages to distinguish between messages about real-world events and non-event messages. Our approach relies on a rich family of aggregate statistics of topically similar message clusters, including temporal, social, topical, and Twitter-centric features. Our large-scale experiments over millions of Twitter messages show the effectiveness of our approach for surfacing real-world event content on Twitter
On the Accuracy of Hyper-local Geotagging of Social Media Content
Social media users share billions of items per year, only a small fraction of
which is geotagged. We present a data- driven approach for identifying
non-geotagged content items that can be associated with a hyper-local
geographic area by modeling the location distributions of hyper-local n-grams
that appear in the text. We explore the trade-off between accuracy, precision
and coverage of this method. Further, we explore differences across content
received from multiple platforms and devices, and show, for example, that
content shared via different sources and applications produces significantly
different geographic distributions, and that it is best to model and predict
location for items according to their source. Our findings show the potential
and the bounds of a data-driven approach to geotag short social media texts,
and offer implications for all applications that use data-driven approaches to
locate content.Comment: 10 page
Trustworthiness Evaluations of Search Results: The Impact of Rank and Misinformation
Users rely on search engines for information in critical contexts, such as
public health emergencies. Understanding how users evaluate the trustworthiness
of search results is therefore essential. Research has identified rank and the
presence of misinformation as factors impacting perceptions and click behavior
in search. Here, we elaborate on these findings by measuring the effects of
rank and misinformation, as well as warning banners, on the perceived
trustworthiness of individual results in search. We conducted three online
experiments (N=3196) using Covid-19-related queries to address this question.
We show that although higher-ranked results are clicked more often, they are
not more trusted. We also show that misinformation did not change trust in
accurate results below it. However, a warning about unreliable sources
backfired, decreasing trust in accurate information but not misinformation.
This work addresses concerns about how people evaluate information in search,
and illustrates the dangers of generic prevention approaches.Comment: 24 pages, 10 figures, 4 supplementary file
Web-Based VR Experiments Powered by the Crowd
We build on the increasing availability of Virtual Reality (VR) devices and
Web technologies to conduct behavioral experiments in VR using crowdsourcing
techniques. A new recruiting and validation method allows us to create a panel
of eligible experiment participants recruited from Amazon Mechanical Turk.
Using this panel, we ran three different crowdsourced VR experiments, each
reproducing one of three VR illusions: place illusion, embodiment illusion, and
plausibility illusion. Our experience and worker feedback on these experiments
show that conducting Web-based VR experiments using crowdsourcing is already
feasible, though some challenges---including scale---remain. Such crowdsourced
VR experiments on the Web have the potential to finally support replicable VR
experiments with diverse populations at a low cost.Comment: The Web Conference 2018 (WWW 2018); update citation forma
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