Media Corpus Research Articles

Conceptualizing the Issues Related to the Mob Lynching of Women in India: A Qualitative and Quantitative Analysis of Media Corpus and Discourse

Conceptualizing the Issues Related to the Mob Lynching of Women in India: A Qualitative and Quantitative Analysis of Media Corpus and Discourse

Representation of autism in Vietnamese digital news media: a computational corpus and framing analysis

ABSTRACT This paper examines news media coverage on autism, a public health issue in Vietnam. Computational corpus analysis and framing analysis of Vietnamese digital news media of over 580,000 words are deemed usefuls method for big data analysis. The language patterns, extracted by WordSmith software, suggests autism is framed primarily as a medical problem and a family issue, not a matter of social policy or an aspect of human diversity. Noticeably, individuals with autism are expected to integrate to fit in with the community, not the other way around, where the society acts as an agent to accommodate autism diversity within an inclusive environment. The study found that the under-represented voices of individuals with autism and family actors, and the dominant voices of healthcare, education, and other professionals, along with government authorities, were almost absent in the media corpus.

School, Studying, and Smarts: Gender Stereotypes and Education Across 80 Years of American Print Media, 1930–2009

Abstract In this article, we apply computational word embeddings to a 200-million-word corpus of American print media (1930–2009) to examine how education-relevant gender stereotypes changed as women’s educational attainment caught up with and eventually surpassed men’s. This case presents a rare opportunity to observe how cultural components of the gender system transform alongside the reversal of an important pattern of stratification. We track six stereotypes that prior work linked to academic outcomes. Our results suggest that stereotypes most closely tied to the core stereotypical distinction between women as communal and men as agentic remained unchanged. The other stereotypes we tracked, however, became increasingly gender polarized: as school and studying gained feminine associations, intelligence and unintelligence gained masculine ones. Unexpectedly, we observe that trends in the gender associations of intelligence and studying are near-perfect mirror opposites, suggesting an interrelationship. We use these observations to further elaborate contemporary theoretical accounts of the gender system, arguing that this system persists partly because stereotypes shift to reinterpret social change in terms of a durable hierarchical distinction between women and men.

Фразеологическая картина мира в русских медиатекстах (на материале Газетного корпуса русского языка)

The article analyzes the most frequent, popular phraseological units that appear in the National Media Corpus. The aim of the study is to determine the quantitative and qualitative nature of these phraseological units, paying attention to their connotative aspect and modality. As the analysis showed, all phraseological units are characterized by expressiveness, but the connotative aspects of appraisal, emotionality, and expressiveness manifest themselves in different ways. The vast majority have a connotative aspect of appraisal, but negative appraisal prevails. Moreover, it should be noted that most of the analyzed phraseological units have an objective assessment, since their use corresponds to their dictionary meaning.

Detecting racial stereotypes: An Italian social media corpus where psychology meets NLP

The generation of stereotypes allows us to simplify the cognitive complexity we have to deal with in everyday life. Stereotypes are extensively used to describe people who belong to a different ethnic group, particularly in racial hoaxes and hateful content against immigrants. This paper addresses the study of stereotypes from a novel perspective that involves psychology and computational linguistics both. On the one hand, it describes an Italian social media corpus built within a social psychology study, where stereotypes and related forms of discredit were made explicit through annotation. On the other hand, it provides some lexical analysis, to bring out the linguistic features of the messages collected in the corpus, and experiments for validating this annotation scheme and its automatic application to other corpora in the future. The main expected outcome is to shed some light on the usefulness of this scheme for training tools that automatically detect and label stereotypes in Italian.

Pojęcie kłamstwa we współczesnych tekstach angielskich

The study aims to reconstruct the linguistic-cultural image of the cultural concept of lie entrenched in contemporary English texts. The research is conducted within the S-Q-T methodology proposed by the Ethnolinguistic School of Lublin. The analysis was carried out by distinguishing semantic aspects of the concept of lie in textual data, interpreting them, and grouping them into semantic profiles. The data consist of the contexts selected from the British National Corpus (BNC), the media corpus News on the Web (NOW), and independently selected texts retrieved from the press and the Internet. The research shows that lie in contemporary English texts is primarily seen from the political perspective where it is closely associated with post-truth. The texts also reveal numerous profiles of lie and its ambiguous axiological assessment. Although in general it is considered unethical, morally unacceptable and axiologically negative, lying is also seen as a necessary element of social life and an integral aspect of politics.

When Self-Driving Fails: Evaluating Social Media Posts Regarding Problems and Misconceptions about Tesla’s FSD Mode

With the recent deployment of the latest generation of Tesla’s Full Self-Driving (FSD) mode, consumers are using semi-autonomous vehicles in both highway and residential driving for the first time. As a result, drivers are facing complex and unanticipated situations with an unproven technology, which is a central challenge for cooperative cognition. One way to support cooperative cognition in such situations is to inform and educate the user about potential limitations. Because these limitations are not always easily discovered, users have turned to the internet and social media to document their experiences, seek answers to questions they have, provide advice on features to others, and assist other drivers with less FSD experience. In this paper, we explore a novel approach to supporting cooperative cognition: Using social media posts can help characterize the limitations of the automation in order to get information about the limitations of the system and explanations and workarounds for how to deal with these limitations. Ultimately, our goal is to determine the kinds of problems being reported via social media that might be useful in helping users anticipate and develop a better mental model of an AI system that they rely on. To do so, we examine a corpus of social media posts about FSD problems to identify (1) the typical problems reported, (2) the kinds of explanations or answers provided by users, and (3) the feasibility of using such user-generated information to provide training and assistance for new drivers. The results reveal a number of limitations of the FSD system (e.g., lane-keeping and phantom braking) that may be anticipated by drivers, enabling them to predict and avoid the problems, thus allowing better mental models of the system and supporting cooperative cognition of the human-AI system in more situations.

Examining the contingency between emotional words and genders: Evidence from the media corpus of #MeToo movement

Gender inequalities are linguistically constructed and indexed through the strategic use of language representing the power structure in the physical world. Recently, #MeTooIndia, an Indian version of the global feminist movement, provided a scholarly opportunity to explore the gender stereotypes of emotional expressions. Even though some researchers have studied the sociocultural dimensions of #MeToo, the corpus-based linguistic analysis of this movement has yet to be explored significantly. Therefore, the current study investigated the discursive association of emotional representation of masculine and feminine genders in media texts. Given the societal nature of Indian patriarchal ideologies, power and gender stereotypes, we hypothesize that Indian mass media, as a powerful political actor, may attribute more positive lexicons to the masculine pronoun he and more negative markers to the feminine pronoun she. Textual genres of #MeToo were extracted from the print media by using the corpus framework of collocational and concordance methods. Our results revealed that, though the news of #MeToo heightened the feminine actor and largely produced the pronoun she in the referential position, the narratives of texts assigned more positive emotive markers to the masculine social actors. Overall, this study concludes that #MeToo as a gender movement has faced strategic linguistic resistance against feminine sexual victims, favouring the masculine actors.

Analysis of the Artistic Elements of Broadcast Hosting Based on Media Speech Corpus

Whether it is speech synthesis research or speech recognition research, it depends on the construction of excellent speech corpus at the back end to some extent. The phonetic corpus includes three forms: text database, phonetic database and acoustic parameter database. Based on phonetic facts, it serves the basic research of language and the processing of language modernization technology through the functions of natural phonetic collection, phonetic annotation, retrieval and statistics. The purpose of the construction of the media corpus is to serve the research of the audio language of the media, especially the prosody research of the host language. Broadcasting and hosting is an important part of radio and television, and the diversity and richness of modern radio and television programs also put forward new requirements for broadcasting and hosting. Broadcasting determines the basic style, purpose and orientation of radio and television news programs. In the media industry, the status of broadcasting and hosting art is gradually highlighted. Under the background of media convergence, broadcasting and hosting art needs to be developed in more advanced technical fields, and the media voice corpus is an important product created by the development of science and technology.

Developing Conflict: Elite Bias in the the Stakeholder Networks of World Bank Projects

World Bank development projects seek to increase the economic prosperity of communities and nations, as well as reduce conflict and promote peace. Yet, the selection, funding, design and governance of these projects is influenced by national governments who may favor certain identity groups over others. As a result, the economic benefits and costs of these projects may not be distributed equitably across stakeholder groups, which may aggravate, instead of moderate, conflict risk. Building on research that examines how elite bias in stakeholder networks (i.e., the favoring of elites at the expense of peripheral stakeholders) can exacerbate conflict, as well as past research that argues development aid can worsen conflict, we examine the impact elite bias in the stakeholder network of World Bank funded projects on conflict between stakeholders. Using a panel dataset of 763 World Bank funded projects across 124 countries and the media corpus of the Global Database of Environment Language and Tone (GDELT) from these countries, we measure conflict and elite bias in the stakeholder network of intergovernmental organizations at a micro-spatial level and analyze how the relationship between these constructs varies in the presence or absence of a World Bank funded project. We further explore the impact of the national composition of project contractors and the importance of state fragility. Our findings provide support for the argument that the World Bank and other financiers of development projects should take the distribution of benefits and costs across identity groups into account in their selection of projects.

Mapping Digital Discourses of the Capital Region of Finland

This article discusses the three Finnish city names Helsinki, Espoo, and Vantaa, and the urban discourses that surround them. The study reveals patterns of socio-spatial differentiation by examining what meanings people attach to these capital region cities and investigating how these meanings are expressed in online discourses. Using the methodological approach of corpus-assisted onomastics (CAO), this study incorporates onomastics, geographical information systems (GIS), and corpus linguistics. This interdisciplinary research also examines how corpus-assisted discourse studies (CADS) and GIS can be combined to reveal and visualize the contextual information and discursive patterns of toponyms. For this investigation, a data set of 2.7 billion words was collected from the Suomi24 Corpus, one of the biggest discussion fora in Finland. The use of social media corpus as a data source increases the authenticity of this research, as the data was not collected specifically for this study. The analysis reveals that the most frequent digital discourses about the cities refer to places and directions, housing, and mobility. The occurrences differ quantitatively and qualitatively between each city. This paper paves the way for future onomastic studies to research actual name usage using this new methodology. Knowledge gained from such research may not only enrich the field of onomastics, but also facilitate more socially sustainable urban planning.

Network analysis approach for exploring dementia representations in the Slovak media.

The aim of the present study is to test the network analysis method on a corpus of media texts with the aim of systematically analysing media representations of dementia in the Slovak media. A word-network was modelled from the corpus of media documents using social network analysis. 244 text-documents (web, print, TV and radio) from 2015 to 2018 (inclusive) were retrieved from the Newton media database using the search words 'dementia' and 'Alzheimer's disease'. Twelve clusters of co-occurring concepts corresponding to individual themes were identified. The results of the analysis provide evidence that biomedical themes such as 'medical diagnosis', 'prevalence of dementia' and 'memory disorders' possess the greatest representational strength and centrality and account for roughly two-thirds of the whole corpus. Results of the present study provide valuable insights into the representation of dementia in the Slovak media but also demonstrate how media representations can be studied using the innovative approach of network analysis.

Machine Learning Techniques for Sentiment Analysis of Code-Mixed and Switched Indian Social Media Text Corpus - A Comprehensive Review

A comprehensive review of sentiment analysis for code-mixed and switched text corpus of Indian social media using machine learning (ML) approaches, based on recent research studies has been presented in this paper. Code-mixing and switching are linguistic behavior shown by the bilingual/multilingual population, primarily in spoken but also in written communication, especially on social media. Code-mixing involves combining lower linguistic units like words and phrases of a language into the sentences of other language (the base language) and code-switching involves switching to another language, for the length of one sentence or more. In code-mixing and switching, a bilingual person takes one or more words or phrases from one language and introduces them into another language while communicating in that language in spoken or written mode. People nowadays express their views and opinions on several issues on social media. In multilingual countries, people express their views using English as well as their native languages. Several reasons can be attributed to code-mixing. Lack of knowledge in one language on a particular subject, being empathetic, interjection and clarification are some to name. Sentiment analysis of monolingual social media content has been carried out for the last two decades. However, during recent years, Natural Language Processing (NLP) research focus has also shifted towards the exploration of code-mixed data, thereby, making code mixed sentiment analysis an evolving field of research. Systems have been developed using ML techniques to predict the polarity of code-mixed text corpus and to fine tune the existing models to improve their performance.

Public perception and usage of the term carbon: Linguistic analysis in an environmental social media corpus

Abstract The main objective of this study was to examine the specialized environmental vocabulary which is incorporated in ordinary discourse found on the Internet. Specifically, we collected English-language data from two social networks, Twitter and Reddit, with a set of environmental keywords which was compiled using the term candidates extraction technique described in Shvets and Wanner (2020). Furthermore, using data from the initial corpus, we built a smaller sample of texts to serve as a ground for a preliminary linguistic analysis of the environmental term carbon. In environmental discourse, carbon is an umbrella term which has acquired multiple senses when it comes to greenhouse gas emissions. Additionally, this term has settled in ordinary communication (Fletcher & Downing, 2011). By means of a fine-grained manual linguistic analysis applied to the data sample, we identified five semantic patterns in the way the general public conceptualizes carbon in the environmental context.

Emergent impoliteness and persuasive emotionality in Polish media discourses

The focus of the paper is to identify and discuss cases of what we call emergent impoliteness and persuasive emotionality based on selected types of discourse strategies in Polish media which contribute to increasingly high negative emotionality in audiences and to the radicalization of language and attitudes when addressing political opponents. The role and function of emotional discourse are particularly foregrounded to identify its persuasive role in media discourses and beyond. Examples discussed are derived from current Polish media texts. The materials are collected from the large Polish monitor media corpus monco.frazeo.pl (Pęzik 2020). The analysis is conducted in terms of quantitative corpus tools (Pęzik 2012, 2014), concerning emotive and media discourse approaches (Lewandowska-Tomaszczyk and Wilson 2013, Lewandowska-Tomaszczyk 2015, 2017a, 2017b). The analysis includes a presentation of the ways mass media construe events (Langacker 1987/1991) in terms of their ideological framing, understood as particular imposed/constructed event models and structures (cf. Gans 1979). Special attention is paid to the negative axiological evaluation of people and events in terms of mostly implicitly persuasive and offensive discourse, including the role emotion clusters of harm, hurt and offence, anger and contempt play in the media persuasive tactics. The research outcomes provide a research basis and categorization of types of emergent impoliteness and persuasive emotionality, which involve implicit persuasion directed at negative emotionality raising with the media public, as identifiedin the analyzed media texts.

Recent changes in London English. An overview of the main lexical, grammar and discourse features of Multicultural London English (MLE)

This paper is intended to provide an overview of the main lexical, grammar and discourse features of the so-called Multicultural London English (MLE), a recent multiethnolect that can be regarded as a new development of London popular speech with the addition of traits from a pool of other sociolects and varieties of English, namely Caribbean and Jamaican English, and with a high proportion of young speakers. The data here analysed have been extracted from multiple sources, such as the London English Corpus (LOE), the Bergen Corpus of London Teenage Language (COLT), dictionaries, magazines, films, TV series, song lyrics and social media, mainly Twitter.
 Particular attention is paid to those grammar and discourse features which can be considered as the most innovative, such as the quotative this is + pronoun, man used as a personal pronoun, the overuse of a set of vocatives (brother, mate, boy, guy(s), bastard, dickhead), the invariant tags innit and you get me, the adjectives proper and bare used as intensifiers, a high presence of negative vernacular forms (ain’t, third person singular don’t), never as negative preterite and a high proportion of negative concord structures. As regards lexis, a wide range of borrowings and loan words from other varieties and languages are recorded together with an excessive amount of general vague nouns and general extenders.

Construction and Application of Media Corpus Based on Big Data

Aiming at the problems of poor data quality and low application rate in the construction of existing media corpus, this paper proposes the construction and application research of media corpus based on big data. Media corpus data are collected, the data are divided into four categories, the heuristic data item column sorting algorithm is introduced to sort all collection processes, the minimum value of data item collection rate is determined, on this basis, the maximum value of quantity in media corpus is determined, and data collection is realized in media corpus data through sliding window. Then, the state characteristics and probability distribution of feature data are determined by dynamic Bayesian network, the relationship between the state variables and dimensions of media corpus data is determined, and the media corpus data state is processed by component to complete the preprocessing of media corpus data; finally, through the application research of storage and encryption of the designed database through big data technology, the storage structure data and encryption secret key are designed to realize the construction and application of media corpus. The experimental results show that the data quality of the media corpus constructed by the proposed method is high, and its application rate has been improved to a certain extent.

CREDBANK: A Large-Scale Social Media Corpus With Associated Credibility Annotations

Social media has quickly risen to prominence as a news source, yet lingering doubts remain about its ability to spread rumor and misinformation. Systematically studying this phenomenon, however, has been difficult due to the need to collect large-scale, unbiased data along with in-situ judgements of its accuracy. In this paper we present CREDBANK, a corpus designed to bridge this gap by systematically combining machine and human computation. Specifically, CREDBANK is a corpus of tweets, topics, events and associated human credibility judgements. It is based on the real-time tracking of more than 1 billion streaming tweets over a period of more than three months, computational summarizations of those tweets, and intelligent routings of the tweet streams to human annotators — within a few hours of those events unfolding on Twitter. In total CREDBANK comprises more than 60 million tweets grouped into 1049 real-world events, each annotated by 30 human annotators. As an example, with CREDBANK one can quickly calculate that roughly 24% of the events in the global tweet stream are not perceived as credible. We have made CREDBANK publicly available, and hope it will enable new research questions related to online information credibility in fields such as social science, data mining and health.

Generate Adjective Sentiment Dictionary for Social Media Sentiment Analysis Using Constrained Nonnegative Matrix Factorization

Although sentiment analysis has attracted a lot of research, little work has been done on social media data compared to product and movie reviews. This is due to the low accuracy that results from the more informal writing seen in social media data. Currently, most of sentiment analysis tools on social media choose the lexicon-based approach instead of the machine learning approach because the latter requires the huge challenge of obtaining enough human-labeled training data for extremely large-scale and diverse social opinion data. The lexicon-based approach requires a sentiment dictionary to determine opinion polarity. This dictionary can also provide useful features for any supervised learning method of the machine learning approach. However, many benchmark sentiment dictionaries do not cover the many informal and spoken words used in social media. In addition, they are not able to update frequently to include newly generated words online. In this paper, we present an automatic sentiment dictionary generation method, called Constrained Symmetric Nonnegative Matrix Factorization (CSNMF) algorithm, to assign polarity scores to each word in the dictionary, on a large social media corpus — digg.com. Moreover, we will demonstrate our study of Amazon Mechanical Turk (AMT) on social media word polarity, using both the human-labeled dictionaries from AMT and the General Inquirer Lexicon to compare our generated dictionary with. In our experiment, we show that combining links from both WordNet and the corpus to generate sentiment dictionaries does outperform using only one of them, and the words with higher sentiment scores yield better precision. Finally, we conducted a lexicon-based sentiment analysis on human-labeled social comments using our generated sentiment dictionary to show the effectiveness of our method.

The use of hēi diào (‘to turn black’) and its related [V diào] forms in social media

Abstract Hēi diào ‘to turn black’ (黑掉) denotes a change-of-state meaning, but in social media, it has a special pragmatic use that emphasizes “bad consequences” following an uncooperative act, whether right or wrong. This metaphorical and new transitive use of hēi diào, which is unique among netizens, has changed the literal status of hēi ‘black’ to other additional accomplishment/achievement meanings. Our study examined the expression hēi diào using data from a social media corpus, the PTT Bulletin Board System. We also analysed all the constructions of hēi diào and similar [V diào (掉)] patterns in the corpus. Unlike most previous work, which analysed the uses of [V diào] only, our study observed pragmatic connotations of hēi diào in social media—uses that only materialized in certain contexts.