News in an online world: The need for an “automatic crap detector”

Chen, Yimin; Conroy, Niall J.; Rubin, Victoria L.

doi:10.1002/pra2.2015.145052010081

Cited by 109 publications

(70 citation statements)

References 5 publications

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“…On the internet, there is much more competition between news sources for the readers' attention (Chen, Conroy, and Rubin 2015). As more and more readers of news articles come from social media networks such as Facebook and Twitter (Mitchell and Page 2015), the need for a good headline that delivers the most clicks grows.…”

Section: Related Workmentioning

confidence: 99%

“…But since many headlines are not read within the context of a newspaper anymore, the function of the headline has shifted. The headline, being one of the primary ways to attract the readers' attention, should above all make the reader curious as to what the article is about, so that it lures the reader into opening the article (Chen, Conroy, and Rubin 2015).…”

Section: Introductionmentioning

confidence: 99%

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Effective Headlines of Newspaper Articles in a Digital Environment

et al. 2017

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We study the effect on click-through rates of applying textual and stylistic features often related to clickbait to headlines of newspaper articles which can be bought in a digital environment. Having a dataset consisting of triples-original headline, rewritten headline, CTR, where CTR is the click-through rate of the rewritten headline in a newsletter from the online kiosk Blendlewe can directly measure whether these "clickbait features" do what they are believed to do: entice readers to click on them. The main findings are as follows. First, the data shows that editors of Blendle indeed often use clickbait features when rewriting headlines. Second, most, but not all, of the clickbait features lead to a statistically significant increase in the number of clicks. Third, predicting the effectiveness of a headline only on the basis of its clickbait features is not possible. The data on which this article is based is publicly available online.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Effective Headlines of Newspaper Articles in a Digital Environment

et al. 2017

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“…The first component includes the evaluation events from 2012-2014 with Wikipedia entries providing fine-grained documentation of the event progression [11] and having substantial representation in the target corpus, the KBA Stream Corpus. 6 Assessors at NIST used the revision history of these pages to construct each topic's nugget set (Section 3.2), forming the ideal summaries. The second component of the data consists of participant runs.…”

Section: Datamentioning

confidence: 99%

“…Furthermore, the rise in popularity of enewspapers and social media has resulted in vast quantities of event-related information being generated and consumed online. On the other hand, much of this published information is redundant, of poor quality, out-of-date or inaccurate [6]. Hence, the general public is increasingly relying on real-time summarization services to get a concise overview of an event, such as the BBC live news timeline, the CNN mobile application, or a government emergency alert system.…”

Section: Introductionmentioning

confidence: 99%

A Study of Realtime Summarization Metrics

Ekstrand-Abueg

McCreadie

Pavlu

et al. 2016

Proceedings of the 25th ACM International on Conference on Information and Knowledge Management

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Unexpected news events, such as natural disasters or other human tragedies, create a large volume of dynamic text data from official news media as well as less formal social media. Automatic real-time text summarization has become an important tool for quickly transforming this overabundance of text into clear, useful information for end-users including affected individuals, crisis responders, and interested third parties. Despite the importance of real-time summarization systems, their evaluation is not well understood as classic methods for text summarization are inappropriate for realtime and streaming conditions.The TREC 2013-2015 Temporal Summarization (TREC-TS) track was one of the first evaluation campaigns to tackle the challenges of real-time summarization evaluation, introducing new metrics, ground-truth generation methodology and dataset. In this paper, we present a study of TREC-TS track evaluation methodology, with the aim of documenting its design, analyzing its effectiveness, as well as identifying improvements and best practices for the evaluation of temporal summarization systems.

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“…The boundary between mainstream media news and user generated content is slowly blurring (Chen et al, 2015b). Kang, Höllerer and O'Donovan (2015) observe that microblogging services have recently transformed from "online journal or peer-communication platforms" to "powerful online information sources operating at a global scale in every aspect of society, largely due to the advance of mobile technologies.…”

Section: Introductionmentioning

confidence: 99%

Deception Detection and Rumor Debunking for Social Media

Rubin¹

2016

The SAGE Handbook of Social Media Research Methods

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The main premise of this chapter is that the time is ripe for more extensive research and development of social media tools that filter out intentionally deceptive information such as deceptive memes, rumors and hoaxes, fake news or other fake posts, tweets and fraudulent profiles. Social media users' awareness of intentional manipulation of online content appears to be relatively low, while the reliance on unverified information (often obtained from strangers) is at an all-time high. I argue there is need for content verification, systematic fact-checking and filtering of social media streams. This literature survey provides a background for understanding current automated deception detection research, rumor debunking, and broader content verification methodologies, suggests a path towards hybrid technologies, and explains why the development and adoption of such tools might still be a significant challenge. Biographical DetailsVictoria L. Rubin is an Associate Professor at the Faculty of Information and Media Studies and the Director of the Language and Information Technologies Research Lab (LiT.RL) at the University of Western Ontario. She specializes in information retrieval and natural language processing techniques that enable analyses of texts to identify, extract, and organize structured knowledge. She studies complex human information behaviors that are, at least partly, expressed through language such as deception, uncertainty, credibility, and emotions. Her research on Deception Detection has been published in recent core workshops on the topic and prominent information science conferences, as well as the

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News in an online world: The need for an “automatic crap detector”

Cited by 109 publications

References 5 publications

Effective Headlines of Newspaper Articles in a Digital Environment

Effective Headlines of Newspaper Articles in a Digital Environment

A Study of Realtime Summarization Metrics

Deception Detection and Rumor Debunking for Social Media

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