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.
@cite_26: Systematically biased editing, persistently maintained, can occur on Wikipedia while nominally following guidelines. Techniques for biasing an entry include deleting positive material, adding negative material, using a one-sided selection of sources, and exaggerating the significance of particular topics. To maintain bias in an entry in the face of resistance, key techniques are reverting edits, selectively invoking Wikipedia rules, and overruling resistant editors. Options for dealing with sustained biased editing include making complaints, mobilizing counterediting, and exposing the bias. To illustrate these techniques and responses, the rewriting of my own Wikipedia entry serves as a case study. It is worthwhile becoming aware of persistent bias and developing ways to counter it in order for Wikipedia to move closer to its goal of providing accurate and balanced information.
@cite_17: Wikipedia advocates a strict “neutral point of view” (NPOV) policy. However, although originally a U.S-based, English-language phenomenon, the online, user-created encyclopedia now has versions in many languages. This study examines the extent to which content and perspectives vary across cultures by comparing articles about famous persons in the Polish and English editions of Wikipedia. The results of quantitative and qualitative content analyses reveal systematic differences related to the different cultures, histories, and values of Poland and the United States; at the same time, a U.S. English-language advantage is evident throughout. In conclusion, the implications of these findings for the quality and objectivity of Wikipedia as a global repository of knowledge are discussed, and recommendations are advanced for Wikipedia end users and content developers. © 2011 Wiley Periodicals, Inc.
@cite_16: Wikipedia is a community-created encyclopedia that contains information about notable people from different countries, epochs and disciplines and aims to document the world's knowledge from a neutral point of view. However, the narrow diversity of the Wikipedia editor community has the potential to introduce systemic biases such as gender biases into the content of Wikipedia. In this paper we aim to tackle a sub problem of this larger challenge by presenting and applying a computational method for assessing gender bias on Wikipedia along multiple dimensions. We find that while women on Wikipedia are covered and featured well in many Wikipedia language editions, the way women are portrayed starkly differs from the way men are portrayed. We hope our work contributes to increasing awareness about gender biases online, and in particular to raising attention to the different levels in which gender biases can manifest themselves on the web.
@cite_8: News entities must select and filter the coverage they broadcast through their respective channels since the set of world events is too large to be treated exhaustively. The subjective nature of this filtering induces biases due to, among other things, resource constraints, editorial guidelines, ideological affinities, or even the fragmented nature of the information at a journalist's disposal. The magnitude and direction of these biases are, however, widely unknown. The absence of ground truth, the sheer size of the event space, or the lack of an exhaustive set of absolute features to measure make it difficult to observe the bias directly, to characterize the leaning's nature and to factor it out to ensure a neutral coverage of the news. In this work, we introduce a methodology to capture the latent structure of media's decision process on a large scale. Our contribution is multi-fold. First, we show media coverage to be predictable using personalization techniques, and evaluate our approach on a large set of events collected from the GDELT database. We then show that a personalized and parametrized approach not only exhibits higher accuracy in coverage prediction, but also provides an interpretable representation of the selection bias. Last, we propose a method able to select a set of sources by leveraging the latent representation. These selected sources provide a more diverse and egalitarian coverage, all while retaining the most actively covered events.
Some research also covers other types of bias, e.g. selection bias @cite_8 , @cite_26 and bias focusing on specific topics, such as gender bias @cite_16 or cultural bias @cite_17 . We do not consider open opinions to be bias. For example, the statement is not bias according to our definition because the writer makes clear that it is her own opinion.