Matches in ScholarlyData for { ?s ?p ?o. }
- 12 creator christopher-horn.
- 12 creator edgardo-ferretti.
- 12 creator elisabeth-lex.
- 12 creator leticia-cagnina.
- 12 creator marcelo-errecalde.
- 12 creator michael-granitzer.
- 12 creator michael-voelske.
- 12 type InProceedings.
- 12 label "Measuring the Quality of Web Content using Factual Information".
- 12 sameAs 12.
- 12 abstract "Nowadays, many decisions are based on information found in the Web. For the most part, the disseminating sources are not certified, and hence an assessment of the quality and credibility of Web content became more important than ever. With factual density we present a simple statistical quality measure that is based on facts extracted from Web content using Open Information Extraction. In a first case study, we use this measure to identify featured/good articles in Wikipedia. We compare the factual density measure with word count, a measure that has successfully been applied to this task in the past. Our evaluation corroborates the good performance of word count in Wikipedia since featured/good articles are often longer than non-featured. However, for articles of similar lengths the word count measure fails while factual density can separate between them with an F-measure of 90.4%. We also investigate the use of relational features for categorizing Wikipedia articles into featured/good versus non-featured ones. If articles have similar lengths, we achieve an F-measure of 86.7% and 84% otherwise.".
- 12 hasAuthorList authorList.
- 12 isPartOf proceedings.
- 12 keyword "Factual Information".
- 12 keyword "Information Quality".
- 12 keyword "Web Content".
- 12 title "Measuring the Quality of Web Content using Factual Information".
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- item-1 hasContent elisabeth-lex.
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- item-2 hasContent michael-voelske.
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- item-3 hasContent marcelo-errecalde.
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- item-4 hasContent edgardo-ferretti.
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- item-5 hasContent leticia-cagnina.
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- item-6 hasContent christopher-horn.
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- item-7 hasContent benno-stein.
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- item-8 hasContent michael-granitzer.
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- 14 creator akshay-dua.
- 14 creator tien-le.
- 14 creator wu-chang-feng.
- 14 type InProceedings.
- 14 label "kaPoW Plugins: Protecting Web Applications Using Reputation-based Proof-of-Work".
- 14 sameAs 14.
- 14 abstract "Comment spam is a fact of life if you have a blog or forum. Tools like Akismet and CAPTCHA help prevent spam in applications like WordPress or phpBB. However, they are not devoid of shortcomings. CAPTCHAs are getting easier to solve by automated adversaries like bots and pose usability issues. Akismet strives to detect spam, but can't do much to reduce it. This paper presents the kaPoW plugin and reputation service that can complement existing anti-spam tools. kaPoW creates disincentives for sending spam by slowing down spammers. It uses a web-based proof-of-work approach wherein a client is given a computational puzzle to solve before accessing a service (e.g. comment posting). The idea is to set puzzle difficulties based on a client's reputation, thereby, issuing ``harder'' puzzles to spammers. The more time spammers solve puzzles, the less time they have to send spam. Unlike CAPTCHAs, kaPoW requires no additional user interaction since all the puzzles are issued and solved in software. kaPoW can be used by any web application that supports an extension framework (e.g. plugins) and is concerned about spam.".
- 14 hasAuthorList authorList.
- 14 isPartOf proceedings.
- 14 keyword "puzzles".
- 14 keyword "reputation".
- 14 keyword "spam".
- 14 title "kaPoW Plugins: Protecting Web Applications Using Reputation-based Proof-of-Work".
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- item-1 hasContent akshay-dua.
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- item-2 hasContent wu-chang-feng.
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- item-3 hasContent tien-le.
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- 15 creator james-caverlee.
- 15 creator krishna-kamath.
- 15 creator kyumin-lee.
- 15 creator zhiyuan-cheng.
- 15 type InProceedings.
- 15 label "Detecting Collective Attention Spam".
- 15 sameAs 15.
- 15 abstract "We examine the problem of collective attention spam, in which spammers target social media where user attention quickly coalesces and then collectively focuses around a phenomenon. Compared to many existing spam types, collective attention spam relies on the users themselves to seek out the content -- like breaking news, viral videos, and popular memes -- where the spam will be encountered, potentially increasing its effectiveness and reach. We study the presence of collective spam in one popular service, Twitter, and we develop spam classifier to detect spam messages generated by collective attention spammers. Since many instances of collective attention are bursty and unexpected, it is difficult to build spam detectors to pre-screen them before they arise; hence, we examine the effectiveness of quickly learning a classifier based on the first moments of a bursting phenomenon. Through initial experiments over a small set of trending topics on Twitter, we find encouraging results, suggesting that collective attention spam may be identified early in its life cycle and shielded from the view of unsuspecting social media users.".
- 15 hasAuthorList authorList.
- 15 isPartOf proceedings.
- 15 keyword "collective attention".
- 15 keyword "social media".
- 15 keyword "spam".