22,407 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
$1.00 per RT #BostonMarathon #PrayForBoston: analyzing fake content on Twitter
This study found that 29% of the most viral content on Twitter during the Boston bombing crisis were rumors and fake content.AbstractOnline social media has emerged as one of the prominent channels for dissemination of information during real world events. Malicious content is posted online during events, which can result in damage, chaos and monetary losses in the real world. We analyzed one such media i.e. Twitter, for content generated during the event of Boston Marathon Blasts, that occurred on April, 15th, 2013. A lot of fake content and malicious profiles originated on Twitter network during this event. The aim of this work is to perform in-depth characterization of what factors influenced in malicious content and profiles becoming viral. Our results showed that 29% of the most viral content on Twitter, during the Boston crisis were rumors and fake content; while 51% was generic opinions and comments; and rest was true information. We found that large number of users with high social reputation and verified accounts were responsible for spreading the fake content. Next, we used regression prediction model, to verify that, overall impact of all users who propagate the fake content at a given time, can be used to estimate the growth of that content in future. Many malicious accounts were created on Twitter during the Boston event, that were later suspended by Twitter. We identified over six thousand such user profiles, we observed that the creation of such profiles surged considerably right after the blasts occurred. We identified closed community structure and star formation in the interaction network of these suspended profiles amongst themselves
Fully Automated Fact Checking Using External Sources
Given the constantly growing proliferation of false claims online in recent
years, there has been also a growing research interest in automatically
distinguishing false rumors from factually true claims. Here, we propose a
general-purpose framework for fully-automatic fact checking using external
sources, tapping the potential of the entire Web as a knowledge source to
confirm or reject a claim. Our framework uses a deep neural network with LSTM
text encoding to combine semantic kernels with task-specific embeddings that
encode a claim together with pieces of potentially-relevant text fragments from
the Web, taking the source reliability into account. The evaluation results
show good performance on two different tasks and datasets: (i) rumor detection
and (ii) fact checking of the answers to a question in community question
answering forums.Comment: RANLP-201
Do You Trust Me(dia)?: How Students Perceive and Identify Fake News
Social media has become an increasingly popular source of news among young adults. However, with the rise of âfake news,â credibility comes into question and young adults are left on their own to determine which news is real and which is false. Two focus groups were employed in this study to gain a greater understanding of how college students aged 18-24 determine what news to trust on social media and the factors that impacted those decisions. Young adults in that age group trust news found on social media based on a variety of factors including the person that is sharing the news, the particular social media site it comes from, and the ability to verify the news with other alternative sources
Investigating Rumor Propagation with TwitterTrails
Social media have become part of modern news reporting, used by journalists
to spread information and find sources, or as a news source by individuals. The
quest for prominence and recognition on social media sites like Twitter can
sometimes eclipse accuracy and lead to the spread of false information. As a
way to study and react to this trend, we introduce {\sc TwitterTrails}, an
interactive, web-based tool ({\tt twittertrails.com}) that allows users to
investigate the origin and propagation characteristics of a rumor and its
refutation, if any, on Twitter. Visualizations of burst activity, propagation
timeline, retweet and co-retweeted networks help its users trace the spread of
a story. Within minutes {\sc TwitterTrails} will collect relevant tweets and
automatically answer several important questions regarding a rumor: its
originator, burst characteristics, propagators and main actors according to the
audience. In addition, it will compute and report the rumor's level of
visibility and, as an example of the power of crowdsourcing, the audience's
skepticism towards it which correlates with the rumor's credibility. We
envision {\sc TwitterTrails} as valuable tool for individual use, but we
especially for amateur and professional journalists investigating recent and
breaking stories. Further, its expanding collection of investigated rumors can
be used to answer questions regarding the amount and success of misinformation
on Twitter.Comment: 10 pages, 8 figures, under revie
Classifying Tweet Level Judgements of Rumours in Social Media
Social media is a rich source of rumours and corresponding community
reactions. Rumours reflect different characteristics, some shared and some
individual. We formulate the problem of classifying tweet level judgements of
rumours as a supervised learning task. Both supervised and unsupervised domain
adaptation are considered, in which tweets from a rumour are classified on the
basis of other annotated rumours. We demonstrate how multi-task learning helps
achieve good results on rumours from the 2011 England riots
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