How much is Wikipedia Lagging Behind News?

Fetahu, Besnik; Anand, Abhijit; Anand, Avishek

doi:10.1145/2786451.2786460

Cited by 21 publications

(15 citation statements)

References 14 publications

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“…whether a word represents negative emotion. Since a word may be in function of different LIWC categories, we chose the most descriptive LIWC category for a word 8 . In general, LIWC categories express a range of psychological and sociological functions of words, and thus, are highly important for subjective tasks like detecting statements with biased language.…”

Section: Statement Representationmentioning

confidence: 99%

Neural Based Statement Classification for Biased Language

Hube

Fetahu

2019

Proceedings of the Twelfth ACM International Conference on Web Search and Data Mining

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Biased language commonly occurs around topics which are of controversial nature, thus, stirring disagreement between the different involved parties of a discussion. This is due to the fact that for language and its use, specifically, the understanding and use of phrases, the stances are cohesive within the particular groups. However, such cohesiveness does not hold across groups.In collaborative environments or environments where impartial language is desired (e.g. Wikipedia, news media), statements and the language therein should represent equally the involved parties and be neutrally phrased. Biased language is introduced through the presence of inflammatory words or phrases, or statements that may be incorrect or one-sided, thus violating such consensus.In this work, we focus on the specific case of phrasing bias, which may be introduced through specific inflammatory words or phrases in a statement. For this purpose, we propose an approach that relies on a recurrent neural networks in order to capture the inter-dependencies between words in a phrase that introduced bias.We perform a thorough experimental evaluation, where we show the advantages of a neural based approach over competitors that rely on word lexicons and other hand-crafted features in detecting biased language. We are able to distinguish biased statements with a precision of P = 0.917, thus significantly outperforming baseline models with an improvement of over 30%. Finally, we release the largest corpus of statements annotated for biased language.

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Section: Statement Representationmentioning

confidence: 99%

Neural Based Statement Classification for Biased Language

Hube

Fetahu

2019

Proceedings of the Twelfth ACM International Conference on Web Search and Data Mining

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“…An investigation of external references from entity pages within English Wikipedia pages (2001)(2002)(2003)(2004)(2005)(2006)(2007) found that 20% were to news articles (Fetahu, Anand, & Anand, 2015). An investigation of external references from entity pages within English Wikipedia pages (2001)(2002)(2003)(2004)(2005)(2006)(2007) found that 20% were to news articles (Fetahu, Anand, & Anand, 2015).…”

Section: Citations To News Storiesmentioning

confidence: 99%

“…Although there are relatively few scholarly citations to newspapers, encyclopedia citations to news stories seem to be more frequent. An investigation of external references from entity pages within English Wikipedia pages (2001)(2002)(2003)(2004)(2005)(2006)(2007) found that 20% were to news articles (Fetahu, Anand, & Anand, 2015).…”

Section: Citations To News Storiesmentioning

confidence: 99%

News stories as evidence for research? BBC citations from articles, Books, and Wikipedia

Kousha

Thelwall

2017

Asso for Info Science & Tech

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Although news stories target the general public and are sometimes inaccurate, they can serve as sources of real-world information for researchers. This article investigates the extent to which academics exploit journalism using content and citation analyses of online BBC News stories cited by Scopus articles. A total of 27,234 Scopus-indexed publications have cited at least one BBC News story, with a steady annual increase. Citations from the arts and humanities (2.8% of publications in 2015) and social sciences (1.5%) were more likely than citations from medicine (0.1%) and science (<0.1%). Surprisingly, half of the sampled Scopus-cited science and technology (53%) and medicine and health (47%) stories were based on academic research, rather than otherwise unpublished information, suggesting that researchers have chosen a lower-quality secondary source for their citations. Nevertheless, the BBC News stories that were most frequently cited by Scopus, Google Books, and Wikipedia introduced new information from many different topics, including politics, business, economics, statistics, and reports about events. Thus, news stories are mediating real-world knowledge into the academic domain, a potential cause for concern.

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“…This is problematic when dealing with long-tail entities, entities not prominent enough to be included in general domain KBs yet important for domainspecific search tasks, e. g. patent search or historical search [25]. The problem is equally acute with emerging entities, i. e. entities that are completely new or are just gaining popularity, as it often takes considerable time for entities to be added to a KB [16,7].…”

Section: Motivationmentioning

confidence: 99%

Discovering Entities with Just a Little Help from You

Singh

Hoffart

Anand

2016

Proceedings of the 25th ACM International on Conference on Information and Knowledge Management

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Linking entities like people, organizations, books, music groups and their songs in text to knowledge bases (KBs) is a fundamental task for many downstream search and mining applications. Achieving high disambiguation accuracy crucially depends on a rich and holistic representation of the entities in the KB. For popular entities, such a representation can be easily mined from Wikipedia, and many current entity disambiguation and linking methods make use of this fact. However, Wikipedia does not contain long-tail entities that only few people are interested in, and also at times lags behind until newly emerging entities are added. For such entities, mining a suitable representation in a fully automated fashion is very difficult, resulting in poor linking accuracy.What can automatically be mined, though, is a high-quality representation given the context of a new entity occurring in any text. Due to the lack of knowledge about the entity, no method can retrieve these occurrences automatically with high precision, resulting in a chicken-egg problem. To address this, our approach automatically generates candidate occurrences of entities, prompting the user for feedback to decide if the occurrence refers to the actual entity in question. This feedback gradually improves the knowledge and allows our methods to provide better candidate suggestions to keep the user engaged. We propose novel human-in-theloop retrieval methods for generating candidates based on gradient interleaving of diversification and textual relevance approaches.We conducted extensive experiments on the FACC dataset, showing that our approaches convincingly outperform carefully selected baselines in both intrinsic and extrinsic measures while keeping users engaged.

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How much is Wikipedia Lagging Behind News?

Cited by 21 publications

References 14 publications

Neural Based Statement Classification for Biased Language

Neural Based Statement Classification for Biased Language

News stories as evidence for research? BBC citations from articles, Books, and Wikipedia

Discovering Entities with Just a Little Help from You

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