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.
Tor is a popular 'darknet', a network that aims to conceal its users' identities and online activities. Darknets are composed of host machines that cannot be accessed by conventional means, which is why the content they host is typically not indexed by traditional search engines like Google and Bing. On Tor, web content and other types of services can anonymously be made available as so-called hidden services. Obviously, where anonymity can be a vehicle for whistleblowers and political dissidents to exchange information, the reverse of the medal is that it also attracts malicious actors. In our research, we aim to develop a detailed understanding of what Tor is being used for. We applied classification and topic model-based text mining techniques to the content of over a thousand Tor hidden services in order to model their thematic organization and linguistic diversity. As far as we are aware, this paper presents the most comprehensive content-based analysis of Tor to date.
Abstract-Many threats in the real world can be related to activities in open sources on the internet. Early detection of threats based on internet information could assist in the prevention of incidents. However, the amount of data in social media, blogs and forums rapidly increases and it is time consuming for security services to monitor all these open sources. Therefore, it is important to have a system that automatically ranks messages based on their threat potential and thereby allows security operators to check these messages more efficiently. In this paper, we present a novel method for detecting threatening messages on Twitter based on trigger keywords and contextual cues. The system was tested on multiple large collections of Dutch tweets. Our experimental results show that our system can successfully analyze messages and recognize threatening content.
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