Big data analytics and international negotiations: Sentiment analysis of Brexit negotiating outcomes

Georgiadou, Elena; Angelopoulos, Spyros; Drake, Helen

doi:10.1016/j.ijinfomgt.2019.102048

Cited by 71 publications

(31 citation statements)

References 46 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Existing research used SA to find sentiments in subjective sentences (Pang and Lee, 2004) and topics (Nasukawa and Yi, 2003), to explore product reviews and determine the user opinions on a product (Haddi et al, 2013: 26), and to grasp collective political preferences of voters, such as the Brexit vote (Georgiadou et al, 2019). Sentiments are found within comments, feedback or critiques, which mainly shed light on the role of emotion in online communication and offline events (Thelwall andBuckley, 2013: 1608).…”

Section: Discussion Of the Samentioning

confidence: 99%

‘Refugees are not welcome’: Digital racism, online place-making and the evolving categorization of Syrians in Turkey

Özdüzen

Korkut

Ozduzen

2020

New Media & Society

View full text Add to dashboard Cite

This article argues that digital publics unleash and bolster everyday racism, creating an unregulated space where anonymity and ubiquity enable the dissemination of racist message. By creating broader visibility and wider reach of racist texts and facilitating more participation for racists, social media platforms such as Twitter normalize gendered and place-based racialization of refugees. Recently, hostility and hate became the norm in derogating the refugee identity on social media platforms. To investigate the complexity of digital racism, this article presents a unique case study on Twitter, capturing the widespread user reactions in the aftermath of the mass resettlement of Syrians in Turkey. It examines varying racialization of Syrians on the Turkish Twittersphere, using sentiment and qualitative content analyses of hashtags and mentions on Syrians, when they hit Twitter trends for Turkey for a year, first, for mundane events and, second, during the Turkish state’s occupation in Northern Syria.

show abstract

Section: Discussion Of the Samentioning

confidence: 99%

‘Refugees are not welcome’: Digital racism, online place-making and the evolving categorization of Syrians in Turkey

Özdüzen

Korkut

Ozduzen

2020

New Media & Society

View full text Add to dashboard Cite

show abstract

“…Evaluating the impact of mass media requires rapid processing of large amounts of textual information, which can be achieved using natural language processing (NLP) and machine learning (machine learning -ML) techniques. These technologies allow users to extract information from large amounts of textual data [1,2], provide content analysis [3,4], personalized access to news [5][6][7], and even support its production and distribution [8,9].…”

Section: Summary (Required)mentioning

confidence: 99%

KazNewsDataset: Single Country Overall Digital Mass Media Publication Corpus

Yakunin¹,

Kalimoldayev²,

Mukhamediev³

et al. 2021

Data

View full text Add to dashboard Cite

Mass media is one of the most important elements influencing the information environment of society. The mass media is not only a source of information about what is happening but is often the authority that shapes the information agenda, the boundaries, and forms of discussion on socially relevant topics. A multifaceted and, where possible, quantitative assessment of mass media performance is crucial for understanding their objectivity, tone, thematic focus and, quality. The paper presents a corpus of Kazakhstan media, which contains over 4 million publications from 36 primary sources (which has at least 500 publications). The corpus also includes more than 2 million texts of Russian media for comparative analysis of publication activity of the countries, also about 4000 sections of state policy documents. The paper briefly describes the natural language processing and multiple-criteria decision-making methods, which are the algorithmic basis of the text and mass media evaluation method, and describes the results of several research cases, such as identification of propaganda, assessment of the tone of publications, calculation of the level of socially relevant negativity, comparative analysis of publication activity in the field of renewable energy. Experiments confirm the general possibility of evaluating the socially significant news, identifying texts with propagandistic content, evaluating the sentiment of publications using the topic model of the text corpus since the area under receiver operating characteristics curve (ROC AUC) values of 0.81, 0.73 and 0.93 were achieved on abovementioned tasks. The described cases do not exhaust the possibilities of thematic, tonal, dynamic, etc., analysis of the considered corpus of texts. The corpus will be interesting to researchers considering both multiple publications and mass media analysis, including comparative analysis and identification of common patterns inherent in the media of different countries.

show abstract

“…While public opinion has traditionally been measured using opinion polls and surveys (Berinsky 2017; Cai et al 2017), the rise of social media as a significant tool for the formation and expression of public opinion has led to increased interest in the use of social media to infer public opinion (Anstead and O’Loughlin 2014; Dubois, Gruzd, and Jacobson 2018; Klašnja et al 2017). A significant body of research in this area has focused on the measurement of the sentiment of Twitter users (Bastos and Mercea 2018; Georgiadou, Angelopoulos, and Drake 2020; Nordheim et al 2018) as it is recognised that Twitter’s popularity, openness, user friendly interface, along with its horizontal and broadly networked structure make it a strong force in public discourse (Park and Kaye 2017; Parmelee and Bichard 2011). We adopt this approach to measuring public opinion in relation to AfCFTA.…”

Section: Public Opinion and Public Policymentioning

confidence: 99%

Understanding Public Sentiment in Relation to the African Continental Free Trade Agreement

2021

View full text Add to dashboard Cite

This study explores public sentiment in relation to the African Continental Free Trade Agreement (ACFTA) by analysing 18,481 tweets mentioning ACFTA over a three-month period. The findings highlight the dominance of actors outside the African continent in the public discourse on ACFTA thus indicating the importance of the African diaspora and foreign interests in framing the debate and influencing public opinion on the continent. They also highlight the salient issues in the public debate on ACFTA to include its potential effects on national economies and jobs as well as the potential for its exploitation by foreign interests. The study also points at a disconnect between governments and politicians promoting ACFTA on the one hand and the average citizen in Africa on the other as it shows a general negative sentiment in all regions and age groups, and more particularly in West Africa and amongst males towards ACFTA.

show abstract

Big data analytics and international negotiations: Sentiment analysis of Brexit negotiating outcomes

Cited by 71 publications

References 46 publications

‘Refugees are not welcome’: Digital racism, online place-making and the evolving categorization of Syrians in Turkey

‘Refugees are not welcome’: Digital racism, online place-making and the evolving categorization of Syrians in Turkey

KazNewsDataset: Single Country Overall Digital Mass Media Publication Corpus

Understanding Public Sentiment in Relation to the African Continental Free Trade Agreement

Contact Info

Product

Resources

About