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Egbert, J.; Biber, D.; Davies, M.: Developing a bottom-up, user-based method of web register classification (2015)
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- Abstract
- This paper introduces a project to develop a reliable, cost-effective method for classifying Internet texts into register categories, and apply that approach to the analysis of a large corpus of web documents. To date, the project has proceeded in 2 key phases. First, we developed a bottom-up method for web register classification, asking end users of the web to utilize a decision-tree survey to code relevant situational characteristics of web documents, resulting in a bottom-up identification of register and subregister categories. We present details regarding the development and testing of this method through a series of 10 pilot studies. Then, in the second phase of our project we applied this procedure to a corpus of 53,000 web documents. An analysis of the results demonstrates the effectiveness of these methods for web register classification and provides a preliminary description of the types and distribution of registers on the web.
- Date
- 4. 8.2015 19:22:04
-
Bhatia, S.; Biyani, P.; Mitra, P.: Identifying the role of individual user messages in an online discussion and its use in thread retrieval (2016)
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- Abstract
- Online discussion forums have become a popular medium for users to discuss with and seek information from other users having similar interests. A typical discussion thread consists of a sequence of posts posted by multiple users. Each post in a thread serves a different purpose providing different types of information and, thus, may not be equally useful for all applications. Identifying the purpose and nature of each post in a discussion thread is thus an interesting research problem as it can help in improving information extraction and intelligent assistance techniques. We study the problem of classifying a given post as per its purpose in the discussion thread and employ features based on the post's content, structure of the thread, behavior of the participating users, and sentiment analysis of the post's content. We evaluate our approach on two forum data sets belonging to different genres and achieve strong classification performance. We also analyze the relative importance of different features used for the post classification task. Next, as a use case, we describe how the post class information can help in thread retrieval by incorporating this information in a state-of-the-art thread retrieval model.
- Date
- 22. 1.2016 11:50:46
-
Johnson, E.H.: S R Ranganathan in the Internet age (2019)
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- Abstract
- S R Ranganathan's ideas have influenced library classification since the inception of his Colon Classification in 1933. His address at Elsinore, "Library Classification Through a Century", was his grand vision of the century of progress in classification from 1876 to 1975, and looked to the future of faceted classification as the means to provide a cohesive system to organize the world's information. Fifty years later, the internet and its achievements, social ecology, and consequences present a far more complicated picture, with the library as he knew it as a very small part and the problems that he confronted now greatly exacerbated. The systematic nature of Ranganathan's canons, principles, postulates, and devices suggest that modern semantic algorithms could guide automatic subject tagging. The vision presented here is one of internet-wide faceted classification and retrieval, implemented as open, distributed facets providing unified faceted searching across all web sites.
-
Schultz, S.: ¬Die eine App für alles : Mobile Zukunft in China (2016)
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- Date
- 22. 6.2018 14:22:02
-
Derek Doran, D.; Gokhale, S.S.: ¬A classification framework for web robots (2012)
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- Abstract
- The behavior of modern web robots varies widely when they crawl for different purposes. In this article, we present a framework to classify these web robots from two orthogonal perspectives, namely, their functionality and the types of resources they consume. Applying the classification framework to a year-long access log from the UConn SoE web server, we present trends that point to significant differences in their crawling behavior.
-
Huang, C.; Fu, T.; Chen, H.: Text-based video content classification for online video-sharing sites (2010)
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- Abstract
- With the emergence of Web 2.0, sharing personal content, communicating ideas, and interacting with other online users in Web 2.0 communities have become daily routines for online users. User-generated data from Web 2.0 sites provide rich personal information (e.g., personal preferences and interests) and can be utilized to obtain insight about cyber communities and their social networks. Many studies have focused on leveraging user-generated information to analyze blogs and forums, but few studies have applied this approach to video-sharing Web sites. In this study, we propose a text-based framework for video content classification of online-video sharing Web sites. Different types of user-generated data (e.g., titles, descriptions, and comments) were used as proxies for online videos, and three types of text features (lexical, syntactic, and content-specific features) were extracted. Three feature-based classification techniques (C4.5, Naïve Bayes, and Support Vector Machine) were used to classify videos. To evaluate the proposed framework, user-generated data from candidate videos, which were identified by searching user-given keywords on YouTube, were first collected. Then, a subset of the collected data was randomly selected and manually tagged by users as our experiment data. The experimental results showed that the proposed approach was able to classify online videos based on users' interests with accuracy rates up to 87.2%, and all three types of text features contributed to discriminating videos. Support Vector Machine outperformed C4.5 and Naïve Bayes techniques in our experiments. In addition, our case study further demonstrated that accurate video-classification results are very useful for identifying implicit cyber communities on video-sharing Web sites.
-
Landwehr, A.: China schafft digitales Punktesystem für den "besseren" Menschen (2018)
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- Date
- 22. 6.2018 14:29:46
-
Andrade, T.C.; Dodebei, V.: Traces of digitized newspapers and bom-digital news sites : a trail to the memory on the internet (2016)
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- Date
- 19. 1.2019 17:42:22
-
Social Media und Web Science : das Web als Lebensraum, Düsseldorf, 22. - 23. März 2012, Proceedings, hrsg. von Marlies Ockenfeld, Isabella Peters und Katrin Weller. DGI, Frankfurt am Main 2012 (2012)
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-
Oguz, F.; Koehler, W.: URL decay at year 20 : a research note (2016)
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- Date
- 22. 1.2016 14:37:14
-
Hartmann, B.: Ab ins MoMA : zum virtuellen Museumsgang (2011)
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- Date
- 3. 5.1997 8:44:22
-
Thelwall, M.; Buckley, K.; Paltoglou, G.: Sentiment in Twitter events (2011)
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- Date
- 22. 1.2011 14:27:06
-
Okoli, C.; Mehdi, M.; Mesgari, M.; Nielsen, F.A.; Lanamäki, A.: Wikipedia in the eyes of its beholders : a systematic review of scholarly research on Wikipedia readers and readership (2014)
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- Date
- 18.11.2014 13:22:03
-
Firnkes, M.: Schöne neue Welt : der Content der Zukunft wird von Algorithmen bestimmt (2015)
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- Date
- 5. 7.2015 22:02:31
-
Evans, H.K.; Ovalle, J.; Green, S.: Rockin' robins : do congresswomen rule the roost in the Twittersphere? (2016)
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- Date
- 22. 1.2016 11:51:19
-
Arbelaitz, O.; Martínez-Otzeta. J.M.; Muguerza, J.: User modeling in a social network for cognitively disabled people (2016)
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- Date
- 22. 1.2016 12:02:26
-
Gorrell, G.; Bontcheva, K.: Classifying Twitter favorites : Like, bookmark, or Thanks? (2016)
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- Abstract
- Since its foundation in 2006, Twitter has enjoyed a meteoric rise in popularity, currently boasting over 500 million users. Its short text nature means that the service is open to a variety of different usage patterns, which have evolved rapidly in terms of user base and utilization. Prior work has categorized Twitter users, as well as studied the use of lists and re-tweets and how these can be used to infer user profiles and interests. The focus of this article is on studying why and how Twitter users mark tweets as "favorites"-a functionality with currently poorly understood usage, but strong relevance for personalization and information access applications. Firstly, manual analysis and classification are carried out on a randomly chosen set of favorited tweets, which reveal different approaches to using this functionality (i.e., bookmarks, thanks, like, conversational, and self-promotion). Secondly, an automatic favorites classification approach is proposed, based on the categories established in the previous step. Our machine learning experiments demonstrate a high degree of success in matching human judgments in classifying favorites according to usage type. In conclusion, we discuss the purposes to which these data could be put, in the context of identifying users' patterns of interests.
-
Dufour, C.; Bartlett, J.C.; Toms, E.G.: Understanding how webcasts are used as sources of information (2011)
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- Date
- 22. 1.2011 14:16:14
-
Zimmer, M.; Proferes, N.J.: ¬A topology of Twitter research : disciplines, methods, and ethics (2014)
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- Date
- 20. 1.2015 18:30:22
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Bhattacharya, S.; Yang, C.; Srinivasan, P.; Boynton, B.: Perceptions of presidential candidates' personalities in twitter (2016)
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- Date
- 22. 1.2016 11:25:47
