Details
| Original language | English |
|---|---|
| Title of host publication | Bias in Information, Algorithms, and Systems |
| Subtitle of host publication | Proceedings of the International Workshop on Bias in Information, Algorithms, and Systems co-located with 13th International Conference on Transforming Digital Worlds (iConference 2018) |
| Pages | 19-23 |
| Number of pages | 5 |
| Publication status | Published - 2018 |
| Event | 2018 International Workshop on Bias in Information, Algorithms, and Systems, BIAS 2018 - Sheffield, United Kingdom (UK) Duration: 25 Mar 2018 → 25 Mar 2018 |
Publication series
| Name | CEUR Workshop Proceedings |
|---|---|
| Publisher | CEUR Workshop Proceedings |
| Volume | 2103 |
| ISSN (Print) | 1613-0073 |
Abstract
Natural language textual corpora depending on their genre, often contain bias which reect the point of view towards a subject of the original content creator. Even for sources like Wikipedia, a collaboratively created encyclopedia, which follows a Neutral Point of View (NPOV) policy, the pages therein are prone to such violations, this due to either: (i) Wikipedia contributors not being aware of NPOV policies or (ii) intentional push towards specific points of views. We present an approach for identifying bias words in online textual corpora using semantic relations of word vectors created through word2Vec. The bias word lists created by our approach help on identifying biased language in online texts.
ASJC Scopus subject areas
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Bias in Information, Algorithms, and Systems: Proceedings of the International Workshop on Bias in Information, Algorithms, and Systems co-located with 13th International Conference on Transforming Digital Worlds (iConference 2018). 2018. p. 19-23 (CEUR Workshop Proceedings; Vol. 2103).
Research output: Chapter in book/report/conference proceeding › Conference contribution › Research › peer review
}
TY - GEN
T1 - Towards Bias Detection in Online Text Corpora
AU - Hube, Christoph
AU - Jäschke, Robert
AU - Fetahu, Besnik
N1 - Funding information: Acknowledgments This work is funded by the ERC Advanced Grant ALEXANDRIA (grant no. 339233), DESIR (grant no. 31081), and H2020 AFEL project (grant no. 687916).
PY - 2018
Y1 - 2018
N2 - Natural language textual corpora depending on their genre, often contain bias which reect the point of view towards a subject of the original content creator. Even for sources like Wikipedia, a collaboratively created encyclopedia, which follows a Neutral Point of View (NPOV) policy, the pages therein are prone to such violations, this due to either: (i) Wikipedia contributors not being aware of NPOV policies or (ii) intentional push towards specific points of views. We present an approach for identifying bias words in online textual corpora using semantic relations of word vectors created through word2Vec. The bias word lists created by our approach help on identifying biased language in online texts.
AB - Natural language textual corpora depending on their genre, often contain bias which reect the point of view towards a subject of the original content creator. Even for sources like Wikipedia, a collaboratively created encyclopedia, which follows a Neutral Point of View (NPOV) policy, the pages therein are prone to such violations, this due to either: (i) Wikipedia contributors not being aware of NPOV policies or (ii) intentional push towards specific points of views. We present an approach for identifying bias words in online textual corpora using semantic relations of word vectors created through word2Vec. The bias word lists created by our approach help on identifying biased language in online texts.
UR - http://www.scopus.com/inward/record.url?scp=85048312610&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:85048312610
T3 - CEUR Workshop Proceedings
SP - 19
EP - 23
BT - Bias in Information, Algorithms, and Systems
T2 - 2018 International Workshop on Bias in Information, Algorithms, and Systems, BIAS 2018
Y2 - 25 March 2018 through 25 March 2018
ER -