134 research outputs found
Tweet, but Verify: Epistemic Study of Information Verification on Twitter
While Twitter provides an unprecedented opportunity to learn about breaking
news and current events as they happen, it often produces skepticism among
users as not all the information is accurate but also hoaxes are sometimes
spread. While avoiding the diffusion of hoaxes is a major concern during
fast-paced events such as natural disasters, the study of how users trust and
verify information from tweets in these contexts has received little attention
so far. We survey users on credibility perceptions regarding witness pictures
posted on Twitter related to Hurricane Sandy. By examining credibility
perceptions on features suggested for information verification in the field of
Epistemology, we evaluate their accuracy in determining whether pictures were
real or fake compared to professional evaluations performed by experts. Our
study unveils insight about tweet presentation, as well as features that users
should look at when assessing the veracity of tweets in the context of
fast-paced events. Some of our main findings include that while author details
not readily available on Twitter feeds should be emphasized in order to
facilitate verification of tweets, showing multiple tweets corroborating a fact
misleads users to trusting what actually is a hoax. We contrast some of the
behavioral patterns found on tweets with literature in Psychology research.Comment: Pre-print of paper accepted to Social Network Analysis and Mining
(Springer
Euskahaldun: Euskararen Aldeko Martxa Baten Sare Sozialetako Islaren Bilketa eta Analisia
This work is motivated by the dearth of research that deals with social media
content created from the Basque Country or written in Basque language. While
social fingerprints during events have been analysed in numerous other
locations and languages, this article aims to fill this gap so as to initiate a
much-needed research area within the Basque scientific community. To this end,
we describe the methodology we followed to collect tweets posted during the
quintessential exhibition race in support of the Basque language. We also
present the results of the analysis of these tweets. Our analysis shows that
the most eventful moments lead to spikes in tweeting activity, producing more
tweets. Furthermore, we emphasize the importance of having an official account
for the event in question, which helps improve the visibility of the event in
the social network as well as the dissemination of information to the Basque
community. Along with the official account, journalists and news organisations
play a crucial role in the diffusion of information.Comment: in Basqu
Mining Social Media for Newsgathering: A Review
Social media is becoming an increasingly important data source for learning
about breaking news and for following the latest developments of ongoing news.
This is in part possible thanks to the existence of mobile devices, which
allows anyone with access to the Internet to post updates from anywhere,
leading in turn to a growing presence of citizen journalism. Consequently,
social media has become a go-to resource for journalists during the process of
newsgathering. Use of social media for newsgathering is however challenging,
and suitable tools are needed in order to facilitate access to useful
information for reporting. In this paper, we provide an overview of research in
data mining and natural language processing for mining social media for
newsgathering. We discuss five different areas that researchers have worked on
to mitigate the challenges inherent to social media newsgathering: news
discovery, curation of news, validation and verification of content,
newsgathering dashboards, and other tasks. We outline the progress made so far
in the field, summarise the current challenges as well as discuss future
directions in the use of computational journalism to assist with social media
newsgathering. This review is relevant to computer scientists researching news
in social media as well as for interdisciplinary researchers interested in the
intersection of computer science and journalism.Comment: Accepted for publication in Online Social Networks and Medi
QMUL-SDS @ SardiStance: Leveraging Network Interactions to Boost Performance on Stance Detection using Knowledge Graphs
This paper presents our submission to the SardiStance 2020 shared task,
describing the architecture used for Task A and Task B. While our submission
for Task A did not exceed the baseline, retraining our model using all the
training tweets, showed promising results leading to (f-avg 0.601) using
bidirectional LSTM with BERT multilingual embedding for Task A. For our
submission for Task B, we ranked 6th (f-avg 0.709). With further investigation,
our best experimented settings increased performance from (f-avg 0.573) to
(f-avg 0.733) with same architecture and parameter settings and after only
incorporating social interaction features -- highlighting the impact of social
interaction on the model's performance
Making the Most of Tweet-Inherent Features for Social Spam Detection on Twitter
Social spam produces a great amount of noise on social media services such as
Twitter, which reduces the signal-to-noise ratio that both end users and data
mining applications observe. Existing techniques on social spam detection have
focused primarily on the identification of spam accounts by using extensive
historical and network-based data. In this paper we focus on the detection of
spam tweets, which optimises the amount of data that needs to be gathered by
relying only on tweet-inherent features. This enables the application of the
spam detection system to a large set of tweets in a timely fashion, potentially
applicable in a real-time or near real-time setting. Using two large
hand-labelled datasets of tweets containing spam, we study the suitability of
five classification algorithms and four different feature sets to the social
spam detection task. Our results show that, by using the limited set of
features readily available in a tweet, we can achieve encouraging results which
are competitive when compared against existing spammer detection systems that
make use of additional, costly user features. Our study is the first that
attempts at generalising conclusions on the optimal classifiers and sets of
features for social spam detection over different datasets
- …