Details
| Original language | English |
|---|---|
| Title of host publication | WWW'12 - Proceedings of the 21st Annual Conference on World Wide Web Companion |
| Pages | 547-548 |
| Number of pages | 2 |
| Publication status | Published - 16 Apr 2012 |
| Event | 21st Annual Conference on World Wide Web, WWW'12 - Lyon, France Duration: 16 Apr 2012 → 20 Apr 2012 |
Publication series
| Name | WWW'12 - Proceedings of the 21st Annual Conference on World Wide Web Companion |
|---|
Abstract
The Web is a very democratic medium of communication allowing everyone to express his or her opinion about any type of topic. This multitude of voices makes it more and more important to detect bias and help Internet users understand the background of information sources. Political bias ofWeb sites, articles, or blog posts is hard to identify straightaway. Manual content analysis conducted by experts is the standard way in political and social science to detect this bias. In this paper we present an automated approach relying on methods from information retrieval and corpus statistics to identify biased vocabulary use. As an example, we analyzed 15 years of parliamentary speeches of the German Bundestag and we investigated whether there is bias towards a political party in major national online newspapers and magazines. The results show that bias exists with respect to vocabulary use and it coincides with human judgement. Copyright is held by the author/owner(s).
Keywords
- Bias detection, News analysis, Vector space model
ASJC Scopus subject areas
- Computer Science(all)
- Computer Networks and Communications
Cite this
- Standard
- Harvard
- Apa
- Vancouver
- BibTeX
- RIS
WWW'12 - Proceedings of the 21st Annual Conference on World Wide Web Companion. 2012. p. 547-548 (WWW'12 - Proceedings of the 21st Annual Conference on World Wide Web Companion).
Research output: Chapter in book/report/conference proceeding › Conference contribution › Research › peer review
}
TY - GEN
T1 - Treehugger or Petrolhead?
T2 - 21st Annual Conference on World Wide Web, WWW'12
AU - Krestel, Ralf
AU - Wall, Alex
AU - Nejdl, Wolfgang
PY - 2012/4/16
Y1 - 2012/4/16
N2 - The Web is a very democratic medium of communication allowing everyone to express his or her opinion about any type of topic. This multitude of voices makes it more and more important to detect bias and help Internet users understand the background of information sources. Political bias ofWeb sites, articles, or blog posts is hard to identify straightaway. Manual content analysis conducted by experts is the standard way in political and social science to detect this bias. In this paper we present an automated approach relying on methods from information retrieval and corpus statistics to identify biased vocabulary use. As an example, we analyzed 15 years of parliamentary speeches of the German Bundestag and we investigated whether there is bias towards a political party in major national online newspapers and magazines. The results show that bias exists with respect to vocabulary use and it coincides with human judgement. Copyright is held by the author/owner(s).
AB - The Web is a very democratic medium of communication allowing everyone to express his or her opinion about any type of topic. This multitude of voices makes it more and more important to detect bias and help Internet users understand the background of information sources. Political bias ofWeb sites, articles, or blog posts is hard to identify straightaway. Manual content analysis conducted by experts is the standard way in political and social science to detect this bias. In this paper we present an automated approach relying on methods from information retrieval and corpus statistics to identify biased vocabulary use. As an example, we analyzed 15 years of parliamentary speeches of the German Bundestag and we investigated whether there is bias towards a political party in major national online newspapers and magazines. The results show that bias exists with respect to vocabulary use and it coincides with human judgement. Copyright is held by the author/owner(s).
KW - Bias detection
KW - News analysis
KW - Vector space model
UR - http://www.scopus.com/inward/record.url?scp=84861023233&partnerID=8YFLogxK
U2 - 10.1145/2187980.2188120
DO - 10.1145/2187980.2188120
M3 - Conference contribution
AN - SCOPUS:84861023233
SN - 9781450312301
T3 - WWW'12 - Proceedings of the 21st Annual Conference on World Wide Web Companion
SP - 547
EP - 548
BT - WWW'12 - Proceedings of the 21st Annual Conference on World Wide Web Companion
Y2 - 16 April 2012 through 20 April 2012
ER -