464 research outputs found
This Just In: Fake News Packs a Lot in Title, Uses Simpler, Repetitive Content in Text Body, More Similar to Satire than Real News
The problem of fake news has gained a lot of attention as it is claimed to
have had a significant impact on 2016 US Presidential Elections. Fake news is
not a new problem and its spread in social networks is well-studied. Often an
underlying assumption in fake news discussion is that it is written to look
like real news, fooling the reader who does not check for reliability of the
sources or the arguments in its content. Through a unique study of three data
sets and features that capture the style and the language of articles, we show
that this assumption is not true. Fake news in most cases is more similar to
satire than to real news, leading us to conclude that persuasion in fake news
is achieved through heuristics rather than the strength of arguments. We show
overall title structure and the use of proper nouns in titles are very
significant in differentiating fake from real. This leads us to conclude that
fake news is targeted for audiences who are not likely to read beyond titles
and is aimed at creating mental associations between entities and claims.Comment: Published at The 2nd International Workshop on News and Public
Opinion at ICWS
The Impact of Crowds on News Engagement: A Reddit Case Study
Today, users are reading the news through social platforms. These platforms
are built to facilitate crowd engagement, but not necessarily disseminate
useful news to inform the masses. Hence, the news that is highly engaged with
may not be the news that best informs. While predicting news popularity has
been well studied, it has not been studied in the context of crowd
manipulations. In this paper, we provide some preliminary results to a longer
term project on crowd and platform manipulations of news and news popularity.
In particular, we choose to study known features for predicting news popularity
and how those features may change on reddit.com, a social platform used
commonly for news aggregation. Along with this, we explore ways in which users
can alter the perception of news through changing the title of an article. We
find that news on reddit is predictable using previously studied sentiment and
content features and that posts with titles changed by reddit users tend to be
more popular than posts with the original article title.Comment: Published at The 2nd International Workshop on News and Public
Opinion at ICWSM 201
Different Spirals of Sameness: A Study of Content Sharing in Mainstream and Alternative Media
In this paper, we analyze content sharing between news sources in the
alternative and mainstream media using a dataset of 713K articles and 194
sources. We find that content sharing happens in tightly formed communities,
and these communities represent relatively homogeneous portions of the media
landscape. Through a mix-method analysis, we find several primary content
sharing behaviors. First, we find that the vast majority of shared articles are
only shared with similar news sources (i.e. same community). Second, we find
that despite these echo-chambers of sharing, specific sources, such as The
Drudge Report, mix content from both mainstream and conspiracy communities.
Third, we show that while these differing communities do not always share news
articles, they do report on the same events, but often with competing and
counter-narratives. Overall, we find that the news is homogeneous within
communities and diverse in between, creating different spirals of sameness.Comment: Published at ICWSM 201
NELA-GT-2018: A Large Multi-Labelled News Dataset for The Study of Misinformation in News Articles
In this paper, we present a dataset of 713k articles collected between
02/2018-11/2018. These articles are collected directly from 194 news and media
outlets including mainstream, hyper-partisan, and conspiracy sources. We
incorporate ground truth ratings of the sources from 8 different assessment
sites covering multiple dimensions of veracity, including reliability, bias,
transparency, adherence to journalistic standards, and consumer trust. The
NELA-GT-2018 dataset can be found at https://doi.org/10.7910/DVN/ULHLCB.Comment: Published at ICWSM 201
Space-time singularities and the axion in the Poincare coset models ISO(2,1)/H
By promoting an invariant subgroup of to a gauge symmetry of a
WZWN action, we obtain the description of a bosonic string moving either in a
curved 4-dimensional space--time with an axion field and curvature
singularities or in 3-dimensional Minkowski space--time.Comment: LaTeX, 6 pages plus 2 figures in a separate postscript file, a LaTeX
error fixe
Examining the Production of Co-active Channels on YouTube and BitChute
A concern among content moderation researchers is that hard moderation
measures, such as banning content producers, will push users to more extreme
information environments. Research in this area is still new, but predominately
focuses on one-way migration (from mainstream to alt-tech) due to this concern.
However, content producers on alt-tech social media platforms are not always
banned users from mainstream platforms, instead they may be co-active across
platforms. We explore co-activity on two such platforms: YouTube and BitChute.
Specifically, we describe differences in video production across 27 co-active
channels. We find that the majority of channels use significantly more moral
and political words in their video titles on BitChute than in their video
titles on YouTube. However, the reasoning for this shift seems to be different
across channels. In some cases, we find that channels produce videos on
different sets of topics across the platforms, often producing content on
BitChute that would likely be moderated on YouTube. In rare cases, we find
video titles of the same video change across the platforms. Overall, there is
not a consistent trend across co-active channels in our sample, suggesting that
the production on alt-tech social media platforms does not fit a single
narrative.Comment: This is a MeLa Lab Technical Repor
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