66,263 research outputs found
Personal life event detection from social media
Creating video clips out of personal content from social media is on the rise. MuseumOfMe, Facebook Lookback, and Google Awesome are some popular examples. One core challenge to the creation of such life summaries is the identification of personal events, and their time frame. Such videos can greatly benefit from automatically distinguishing between social media content that is about someone's own wedding from that week, to an old wedding, or to that of a friend. In this paper, we describe our approach for identifying a number of common personal life events from social media content (in this paper we have used Twitter for our test), using multiple feature-based classifiers. Results show that combination of linguistic and social interaction features increases overall classification accuracy of most of the events while some events are relatively more difficult than others (e.g. new born with mean precision of .6 from all three models)
Novel and topical business news and their impact on stock market activities
We propose an indicator to measure the degree to which a particular news
article is novel, as well as an indicator to measure the degree to which a
particular news item attracts attention from investors. The novelty measure is
obtained by comparing the extent to which a particular news article is similar
to earlier news articles, and an article is regarded as novel if there was no
similar article before it. On the other hand, we say a news item receives a lot
of attention and thus is highly topical if it is simultaneously reported by
many news agencies and read by many investors who receive news from those
agencies. The topicality measure for a news item is obtained by counting the
number of news articles whose content is similar to an original news article
but which are delivered by other news agencies. To check the performance of the
indicators, we empirically examine how these indicators are correlated with
intraday financial market indicators such as the number of transactions and
price volatility. Specifically, we use a dataset consisting of over 90 million
business news articles reported in English and a dataset consisting of
minute-by-minute stock prices on the New York Stock Exchange and the NASDAQ
Stock Market from 2003 to 2014, and show that stock prices and transaction
volumes exhibited a significant response to a news article when it is novel and
topical.Comment: 8 pages, 6 figures, 2 table
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
Code wars: steganography, signals intelligence, and terrorism
This paper describes and discusses the process of secret communication known as steganography. The argument advanced here is that terrorists are unlikely to be employing digital steganography to facilitate secret intra-group communication as has been claimed. This is because terrorist use of digital steganography is both technically and operationally implausible. The position adopted in this paper is that terrorists are likely to employ low-tech steganography such as semagrams and null ciphers instead
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