Enhancing Bias Detection in Political News Using Pragmatic Presupposition (2020)

Abstract: Usage of presuppositions in social media and news discourse can be a powerful way to influence the readers as they usually tend to not examine the truth value of the hidden or indirectly expressed information. Fairclough and Wodak (1997) discuss presupposition at a discourse level where some implicit claims are taken for granted in the explicit meaning of a text or utterance. From the Gricean perspective, the presuppositions of a sentence determine the class of contexts in which the sentence could be felicitously uttered. This paper aims to correlate the type of knowledge presupposed in a news article to the bias present in it. We propose a set of guidelines to identify various kinds of presuppositions in news articles and present a dataset consisting of 1050 articles which are annotated for bias (positive, negative or neutral) and the magnitude of presupposition. We introduce a supervised classification approach for detecting bias in political news which significantly outperforms the existing systems.

## introduction in today's situation where we see several instances of social media being used to interfere with politics in controversial ways, the platforms that have been considered as sources of information are now often seen as politically biased. especially in newspapers and news websites, sometimes the reporters tend emphasize more on particular view points selectively, and present biased information which is aligned with their personal political ideology. this can lead to widespread alteration of mass political opinion and impact the decision of the voters. in this paper, we aim to establish a correlation between presupposition and bias in political news articles, and use the knowledge of presupposition to enhance the task of automatic bias detection. presuppositions are linguistic tools whose function is to enable us to take some information for granted without actually asserting it. for instance, consider the utterance "sam will visit california again". this utterance presupposes that sam has visited california before, and asserts that he will visit once again in future. based on their function in discourse, @xcite classifies presuppositions into two levels -semantic and pragmatic. the propositions which the reader or listener assumes to be true come under the class of semantic presuppositions. on the other hand, he defines pragmatic presupposition as "the proposition that a writer or a speaker has taken its truth value for granted in his statement. it consists of previous information about the knowledge, beliefs, ideology and scale of values that the reader or listener must be acquainted with in order to understand the meaning". the notion of pragmatic presupposition is highly useful in analysing media and political discourse such as news articles and election campaign speeches. using them in an article or a speech could be an indicator of some hidden intentions and strategies, such as avoiding some key information, or manipulating the audience to focus on certain aspects which favour the speaker by indirectly suggesting that they are true. similar to this classification is another popular dichotomy which is widely used for studying implicature as conventional or conversational. this idea is extended to the context of presuppositions, based on how they arise @xcite . @xcite define a presupposition as conventional when the presuppositional content arises due to the properties of lexical items present in a sentence. in their view, "certain lexical items have, in addition to their truth content, a special presuppositional content, which is carried through the compositional process to produce a propositional presupposition." on the other hand, there can be some presuppositions which do not contain any lexical triggers. @xcite defines them as conversational presuppositions. he suggests that they are the "inferences which are licensed by general conversational principles, in combination with the truth conditions of the presupposing utterance". ## related work though there have been several speculations in the linguistic research community about the extra linguistic information provided by the use of presuppositions, very few of them are backed up with proper surveys and observations. the initial direction towards such research was motivated by van dijk's idea that in critical discourse analysis, one should closely look at the propositions which in turn suggest some other propositions to be true, but in fact are either not true or controversial. he pointed out some examples from opinion discourse @xcite . for instance, the editorial sections of news usually contain a lot of such propositions which aid in persuading the reader to agree to the given interpretation of some news in the editorial. @xcite conducted a study on how presuppositions can make newspaper advertisements more effective by compensating for the small place occupied by them. he argued that when an advertisement directs the readers to infer some data which is not directly mentioned, they tend to pay more attention to the product being advertised. @xcite took a small sample of data from 5 newspapers and analysed the use of presuppositions in the articles. he manually analysed how presuppositions can contribute in differentiating between the styles of reporting in the pro-government and anti-government stance of the newspaper. however, none of these studies have tested the validity of their claims on a large corpus and no computational work has been done in this domain so far. moreover, all of the above research was carried out for english news, and there has been little work on politics and news discourse in telugu, which is a low resource language. mukku et al. (2016) applied ml techniques for sentiment analysis of telugu news articles. @xcite carried out a case study on political influence through linguistic choices on a corpus of election campaign speeches. @xcite proposed an attention mechanism to detection of bias in telugu news articles. to our knowledge there has been no work on presupposition in telugu till date. our research is the first of its kind which proposes guidelines to identify presuppositions in political news and use that information to enhance the computational methods to detect bias in political news articles. ## corpus creation and annotation to validate our idea computationally, we need a large dataset of news articles which have been annotated for their bias and magnitude of presupposition. there is no such dataset which captures both the features, so we took the corpus foot_0 created by @xcite and modified it. it consists of 1329 articles collected from various newspapers in telugu, a dravidian language spoken widely in telangana and andhra pradesh in india. each article was annotated with a label out of the 6 labels they chose -bjp, tdp, congress, trs, ycp and none. the first five labels represent the bias towards or against those parties (marked by "positive" or "negative" in their dataset), and "none" denotes that the article is unbiased. we created a modified version of the corpus according to our requirement as follows. the original corpus consisted of 218 unbiased articles and 1111 articles which had bias towards some party. out of those, it was found that some were very short, and some had very little or no mention about any political parties or events. such articles were filtered out and we were left with 1050 articles of which 850 were biased and 200 were unbiased. since our main aim was to see the the contribution of presupposition to the biased content in the article, we did not keep the existing labels of "positive" and "negative" to denote the direction of bias. all the biased articles were labelled with a bias label of 1 and unbiased articles with 0. our annotated corpus foot_1 is publicly available to ensure reproducibility of the results and to facilitate further research in this domain. ## experiments our goal is to detect political bias in an article with and without the presupposition information, and compare the results. for this purpose, similar to @xcite , we label political bias detection as a classification problem. the presence or absence of bias (0 or 1) is treated as the label, and the task is to assign an appropriate label to a news article. the first step is to represent each article as a vector. since each vector can be extremely large and sparse, chi-square feature selection algorithm applied, which reduces the size of the vector to 10000. we performed experiments with the following six classifers: 5. random forest classifier: random forest (rf) is an ensemble of decision trees, which are structures that use a tree-like model for the decisions and likely outcomes. random forests construct multiple decision trees and take each of their prediction into consideration for giving the final output. for training purpose, scikit-learn @xcite implementations have been used for all the classifiers with default hyperparameters. we conducted each experiment four times, each differing in the input given to the classifier. following are the four categories of inputs which were used: ## conclusions and future work in this paper, we came up with an interesting correlation between bias and presupposition in news articles. we proved that pragmatic presupposition contributes towards bias in a news article. by using this information, we came up with a supervised method for automatic detection of bias in news articles along with exhaustive guidelines to identify and annotate presuppositions, and a manually annotated dataset to enable further research. the results of our experiments show that our model significantly outperforms all the previous models. though we used only news articles for our experiments, our idea is also applicable to other forms of opinion discourse such as social media texts, reviews, blogs, etc. where bias in text could lead to spread of misinterpreted information at various levels.