online discourse
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Semantic Web ◽  
2022 ◽  
pp. 1-35
Author(s):  
Katarina Boland ◽  
Pavlos Fafalios ◽  
Andon Tchechmedjiev ◽  
Stefan Dietze ◽  
Konstantin Todorov

Analyzing statements of facts and claims in online discourse is subject of a multitude of research areas. Methods from natural language processing and computational linguistics help investigate issues such as the spread of biased narratives and falsehoods on the Web. Related tasks include fact-checking, stance detection and argumentation mining. Knowledge-based approaches, in particular works in knowledge base construction and augmentation, are concerned with mining, verifying and representing factual knowledge. While all these fields are concerned with strongly related notions, such as claims, facts and evidence, terminology and conceptualisations used across and within communities vary heavily, making it hard to assess commonalities and relations of related works and how research in one field may contribute to address problems in another. We survey the state-of-the-art from a range of fields in this interdisciplinary area across a range of research tasks. We assess varying definitions and propose a conceptual model – Open Claims – for claims and related notions that takes into consideration their inherent complexity, distinguishing between their meaning, linguistic representation and context. We also introduce an implementation of this model by using established vocabularies and discuss applications across various tasks related to online discourse analysis.


2021 ◽  
Vol 7 (6) ◽  
pp. 489-498
Author(s):  
Taghreed Abdulasalam ◽  
Istqlal Hassan Ja’afar

The present paper aims to investigate how racial humor is triggered in racial jokes posted online. Racial jokes and the ways it is triggered is an under-researched topic in comparison to the quickly developing literature about other types of racist language.  Thus, one of the main problems this thesis attempt to address is English as a Lingua Franca (ELF) users’ potential lack of awareness of the racially sensitive issues and how to deal with them in (online) intercultural communication. The paper analyzes (312) racial jokes, collected from eight different racial Joke accounts on Twitter. After in-depth reading and a systematic coding process of the dataset, three types of racial jokes were distinguished. These are superiority-based triggers, incongruity-based triggers, and blended triggers. These three different types were found to perform two different functions: racial stereotype reinforcement and racial stereotype challenge.


2021 ◽  
Vol 7 (4) ◽  
pp. 205630512110638
Author(s):  
Catherine Buerger

This article examines the Facebook group #jagärhär, a Sweden-based collective of thousands of people who have made a regular practice of responding en masse to what they regard as hateful comments online. #jagärhar is one of the largest and best-organized collective efforts to respond directly to hatred online anywhere in the world. Drawing on data collected through ethnographic observation and interviews, the article explores two primary research questions: (1) how do the external counterspeech actions of group members work to counter hatred (and, sometimes, misinformation)? and (2) how do the internal practices of the group keep members engaged? I argue that instead of focusing their work on preventing future hateful speech (presumably by changing the minds or incentives of those who post it), #jagärhär members fight against its effects—attempting to lessen the impact of the hateful speech by hiding it in the comment threads, speaking to the “movable middle” rather than those posting hatred, and encouraging more counterspeech against it.


2021 ◽  
pp. 174276652110399
Author(s):  
Jane O’Boyle ◽  
Carol J Pardun

A manual content analysis compares 6019 Twitter comments from six countries during the 2016 US presidential election. Twitter comments were positive about Trump and negative about Clinton in Russia, the US and also in India and China. In the UK and Brazil, Twitter comments were largely negative about both candidates. Twitter sources for Clinton comments were more frequently from journalists and news companies, and still more negative than positive in tone. Topics on Twitter varied from those in mainstream news media. This foundational study expands communications research on social media, as well as political communications and international distinctions.


2021 ◽  
Vol 2020 (1) ◽  
pp. 185
Author(s):  
Hugh Kirkwood

Thousands of non-Japanese nationals work as assistant language teachers (ALTs) in schools throughout Japan. To better understand ALTs’ teaching contexts and motivations, the researcher created a corpus of online discourse about ALTs and used corpus software to identify and analyse key words in context. He also asked questions from critical discourse analysis to examine the relationship of these key words to ideology and power. The findings were that while the discourse often described poor employment conditions and problems for ALTs working in Japanese schools, the discourse itself may also be contributing to the reproduction of these conditions. This is because it seemed to both stigmatise ALTs as fundamentally unprofessional and suggest that ALT positions can be a step towards other types of employment in Japan. Such discourse may encourage people to become ALTs and tolerate poor conditions in the short-term instead of engaging in collective actions to make long-term improvements. 日本で外国語指導助手(ALT)として働く外国籍労働者は何千といる。ALTが働く環境と動機付けを理解するため、筆者はALTに関するディスコースのコーパスを構築し、コーパス分析ソフトを用いて文脈中のキーワードの特定と分析を行った。また批判的言説分析を用いて、抽出されたキーワードとイデオロギー及び影響力の関係を検証した。結果、ディスコースにはALTの劣悪な労働環境と日本の学校で働く上での問題が多くみられた一方で、ディスコース自体がこうした状況の再生産に寄与している可能性が示唆された。ディスコースにより、ALTは基本的に高度な専門性を必要としないというスティグマを形成しうることに加え、ALTは日本で他の職を得るためのステップとなりうることが示唆されているようであった。このようなディスコースは長期的な状況改善のための集団的行動を起こすのではなく、ALTが短期的に現状に我慢することを促している可能性がある。


2021 ◽  
Vol 13 (14) ◽  
pp. 8045
Author(s):  
Yuqin Yang ◽  
Jan van Aalst ◽  
Carol Chan

This study examines the problem of the fragmentation of asynchronous online discourse by using the Knowledge Connection Analyzer (KCA) framework and tools and explores how students could use the KCA data in classroom reflections to deepen their knowledge building (KB) inquiry. We applied the KCA to nine Knowledge Forum® (KF) databases to examine the framework, identify issues with online discourse that may inform further development, and provide data on how the tools work. Our comparisons of the KCA data showed that the databases with more sophisticated teacher–researcher co-design had higher KCA indices than those with regular KF use, validating the framework. Analysis of KF discourse using the KCA helped identify several issues including limited collaboration among peers, underdeveloped practices of synthesizing and rising above of collective ideas, less analysis of conceptual development of discussion threads, and limited collaborative reflection on individual contribution and promising inquiry direction. These issues that open opportunities for further development cannot be identified by other present analytics tools. The exploratory use of the KCA in real classroom revealed that the KCA can support students’ productive reflective assessment and KB. This study discusses the implications for examining and scaffolding online discussions using the KCA assessment framework, with a focus on collective perspectives regarding community knowledge, synthesis, idea improvement, and contribution to community understanding.


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