scholarly journals Pride, Love, and Twitter Rants: Combining Machine Learning and Qualitative Techniques to Understand What Our Tweets Reveal about Race in the US

2019 ◽  
Vol 16 (10) ◽  
pp. 1766 ◽  
Author(s):  
Thu T. Nguyen ◽  
Shaniece Criss ◽  
Amani M. Allen ◽  
M. Maria Glymour ◽  
Lynn Phan ◽  
...  
Keyword(s):  
Content Analysis ◽  
Social Context ◽  
Qualitative Content Analysis ◽  
Middle Eastern ◽  
Stochastic Gradient Descent ◽  
The Social ◽  
The Us ◽  
Positive Sentiment ◽  
Negative Sentiment ◽  
Media Data

Objective: Describe variation in sentiment of tweets using race-related terms and identify themes characterizing the social climate related to race. Methods: We applied a Stochastic Gradient Descent Classifier to conduct sentiment analysis of 1,249,653 US tweets using race-related terms from 2015–2016. To evaluate accuracy, manual labels were compared against computer labels for a random subset of 6600 tweets. We conducted qualitative content analysis on a random sample of 2100 tweets. Results: Agreement between computer labels and manual labels was 74%. Tweets referencing Middle Eastern groups (12.5%) or Blacks (13.8%) had the lowest positive sentiment compared to tweets referencing Asians (17.7%) and Hispanics (17.5%). Qualitative content analysis revealed most tweets were represented by the categories: negative sentiment (45%), positive sentiment such as pride in culture (25%), and navigating relationships (15%). While all tweets use one or more race-related terms, negative sentiment tweets which were not derogatory or whose central topic was not about race were common. Conclusion: This study harnesses relatively untapped social media data to develop a novel area-level measure of social context (sentiment scores) and highlights some of the challenges in doing this work. New approaches to measuring the social environment may enhance research on social context and health.

Upsettling Canada in 140 Characters: race relations in the Twittersphere

2021 ◽  
Author(s):  
Caroline Carter
Keyword(s):  
Content Analysis ◽  
Social Networking ◽  
Race Relations ◽  
Online Discussion ◽  
Qualitative Content Analysis ◽  
Vital Role ◽  
The Social ◽  
Colonial Theory ◽  
Networking Tool ◽  
A Site

This paper is a qualitative content analysis of public tweets made during the Indigenous social movement, Idle No More, containing the #upsettler and #upsettlers hashtags. Using settler colonial theory coupled with previous literature on Twitter during social movements as a guiding framework, this study identifies how settler colonial relations were being constructed on Twitter and how functions of the social networking tool such as the hashtag impacted this process. By examining and analyzing the content of 278 tweets, this study illustrates that Twitter is a site where conversations about race relations in Canada are taking place and that the use of the hashtag function plays a vital role in expanding the reach of this online discussion and creating a sense of solidarity or community among users.

scholarly journals Constructing the emotions of childhood: an analysis of Margaret Mahy’s use of emotion in her literary contribution to the School Journal

2021 ◽  
Author(s):  
◽  
Gemma Amy Helleur Hiscock
Keyword(s):  
Content Analysis ◽  
Social Construction ◽  
Research Study ◽  
Qualitative Content Analysis ◽  
School Librarians ◽  
Reader Responses ◽  
Analysis Methodology ◽  
The Social ◽  
School Journal ◽  
Margaret Mahy

<p>This qualitative content analysis research study examines how Margaret Mahy used emotion in the School Journal to form insights into reader appeal, reader response and the social construction of childhood. This research study examines Mahy’s contribution to the School Journal. The study explores this body of work in terms of how its author uses emotion to captivate readers by evoking the feelings associated with childhood. The underlying objective of the study was to provide insights into why Mahy’s work is so treasured and memorable; to explain how she uses emotion to captivate readers, and how this contributes to the social construction of childhood. The prose and poetry Mahy contributed to the School Journal prove to be a significant, rich and uncharted resource for the purposes of this research investigation. Analysis of this body of work has allowed for greater insights and understanding into Mahy’s contribution to children’s literature. It has also allowed for a greater appreciation of how Mahy’s use of emotion contributes to the social construction of childhood. This type of content analysis research study proves to be invaluable in the development of reader’s advisory services to young people. The employment of a content analysis methodology, underpinned by a discourse analysis approach, enabled the emotional narratives of Mahy’s text to be explained and understood. The study’s findings, that lightness and aliveness are the most prevalent and persuasive emotions operating within Mahy’s text, was substantiated through analysis of actual reader responses. This investigation is most applicable to school librarians, children’s librarians and educators. The study has broader implications for the improvement of client interaction and collection development in youth library services</p>

scholarly journals Navigating identity within the context of culture & community: an examination of creative engagement among black 2SLGBTQ Caribbean-Canadian artists

2021 ◽  
Author(s):  
Shaniqua (Nika) Smith
Keyword(s):  
Content Analysis ◽  
Qualitative Content Analysis ◽  
Community Building ◽  
Secondary Data ◽  
Creative Expression ◽  
Data Sources ◽  
The Social ◽  
Secondary Data Sources ◽  
And Gender ◽  
Equity And Inclusion

This research examines some of the ways Black 2SLGBTQ Caribbean-Canadian artists engage with creative expression to navigate their sexual and gender identities. This study also highlighted the intersection of race, gender, sexual identity, and immigration. The secondary data sources collected were a photography series produced by Jamaican-Canadian photographer Brianna Roye; and a 2015 interview featuring Michèle Pearson Clarke, a Trinidadian-Canadian artist. These secondary data sources were analyzed using multi-textual analysis and qualitative content analysis tools. The findings highlight the potential for art and creative expression to address issues of anti-Black racism and heterosexism, in addition to fostering healing and community building. This study aims to present insight that will contribute to ongoing efforts within the social work profession to promote Black 2SLGBTQ equity and inclusion.

Framing the news on the Syrian War: A comparative study of antiwar.com and cnn.com editorials

2019 ◽  
Vol 13 (4) ◽  
pp. 487-505
Author(s):  
Rayeheh Alitavoli
Keyword(s):  
Content Analysis ◽  
Comparative Study ◽  
Iraq War ◽  
Military Intervention ◽  
Qualitative Content Analysis ◽  
Strategic Plan ◽  
Framing Theory ◽  
Chemical Weapons ◽  
Negative Consequences ◽  
The Us

This study identifies the dominant frames presented in opinion articles published from 20 August to 17 September 2013 on the alternative website – antiwar.com – and the mainstream website – cnn.com; this timeframe includes articles published a week before and a week after the US administration’s decision to attack and withdraw from Syria. The article employs qualitative content analysis and Entman’s framing theory to code the data and extract the themes and dominant frames present in a total of 87 opinion articles. The study concludes that cnn.com provided frames that presented Bashar al-Assad as a ‘brutal villain’ who uses chemical weapons on his own people, while providing frames that stress Barack Obama’s incompetency in carrying out a strategic plan and highlight the negative consequences of a strike. However, antiwar.com articles are more resonant and consistent than cnn.com articles, and provide frames that encourage readers to protest against engaging in another war, reminding them of the failures of similar past wars such as the Iraq War and its negative consequences, as well as stressing the major players that benefited from a military intervention.

scholarly journals Mining social media data to investigate patient perceptions regarding DMARD pharmacotherapy for rheumatoid arthritis

2020 ◽  
Vol 79 (11) ◽  
pp. 1432-1437 ◽  
Author(s):  
Chanakya Sharma ◽  
Samuel Whittle ◽  
Pari Delir Haghighi ◽  
Frada Burstein ◽  
Roee Sa'adon ◽  
...  
Keyword(s):  
Rheumatoid Arthritis ◽  
Social Media ◽  
Side Effects ◽  
Sentiment Analysis ◽  
Computer Algorithms ◽  
Social Media Platforms ◽  
Synthetic Agents ◽  
Positive Sentiment ◽  
Negative Sentiment ◽  
Media Data

ObjectivesWe hypothesise that patients have a positive sentiment regarding biological/targeted synthetic disease modifying anti-rheumatic drugs (b/tsDMARDs) and a negative sentiment towards conventional synthetic agents (csDMARDs). We analysed discussions on social media platforms regarding DMARDs to understand the collective sentiment expressed towards these medications.MethodsTreato analytics were used to download all available posts on social media about DMARDs in the context of rheumatoid arthritis. Strict filters ensured that user generated content was downloaded. The sentiment (positive or negative) expressed in these posts was analysed for each DMARD using sentiment analysis. We also analysed the reason(s) for this sentiment for each DMARD, looking specifically at efficacy and side effects.ResultsComputer algorithms analysed millions of social media posts and included 54 742 posts about DMARDs. We found that both classes had an overall positive sentiment. The ratio of positive to negative posts was higher for b/tsDMARDs (1.210) than for csDMARDs (1.048). Efficacy was the most commonly mentioned reason in posts with a positive sentiment and lack of efficacy was the most commonly mentioned reason for a negative sentiment. These were followed by the presence/absence of side effects in negative or positive posts, respectively.ConclusionsPublic opinion on social media is generally positive about DMARDs. Lack of efficacy followed by side effects were the most common themes in posts with a negative sentiment. There are clear reasons why a DMARD generates a positive or negative sentiment, as the sentiment analysis technology becomes more refined, targeted studies could be done to analyse these reasons and allow clinicians to tailor DMARDs to match patient needs.

Thematic Analysis

2021 ◽  
pp. 283-288
Author(s):  
Jean-Frédéric Morin ◽  
Christian Olsson ◽  
Ece Özlem Atikcan
Keyword(s):  
Social Sciences ◽  
Content Analysis ◽  
Thematic Analysis ◽  
Qualitative Data ◽  
Qualitative Content Analysis ◽  
Analytic Method ◽  
The Social ◽  
Distinct Method

This chapter evaluates thematic analysis (TA), which is one of the oldest and most widely used qualitative analytic method across the social sciences. TA is a flexible method for identifying and analysing patterns of meaning — ‘themes’ — in qualitative data, with wide-ranging applications. The method has a long, if indeterminate, history in the social sciences, but seems likely to have evolved from early forms of (qualitative) content analysis. TA is now more likely to be demarcated and acknowledged as a distinct method; however, confusion remains about what TA is. The popularity of TA as a distinct method received a considerable boost from the publication of Using Thematic Analysis in Psychology by social psychologists Virginia Braun and Victoria Clarke in 2006, which has become one of the most cited academic papers of recent decades.

scholarly journals Degrees of Freedom: Exploring Agency, Narratives, and Technological Affordances in the #TakeAKnee Controversy

2019 ◽  
Vol 5 (2) ◽  
pp. 205630511982612 ◽  
Author(s):  
Judith E. Rosenbaum
Keyword(s):  
Social Media ◽  
Content Analysis ◽  
Degrees Of Freedom ◽  
Qualitative Content Analysis ◽  
Structuration Theory ◽  
The Social ◽  
American National Identity ◽  
Social Media Platforms ◽  
Media Narratives ◽  
Technological Affordances

This study extends current research into social media platforms as counterpublic spaces by examining how the social media narratives produced by the #TakeAKnee controversy negotiate technological affordances and existing discourses surrounding American national identity. Giddens’ Structuration Theory is used to explore the nature of user agency on social media platforms and the extent to which this agency is constrained or enabled by the interplay between the systems and structures that guide social media use. Exploratory qualitative content analysis was used to analyze and compare tweets and Instagram posts using the #TakeAKnee hashtag shared in September 2017. Results showed that narratives are dominated by four themes, freedom, unity, equality and justice, and respect and honor. Users actively employ technological affordances to create highly personalized meanings, affirming that agency operates at the intersection of reflexivity and self-efficacy.

scholarly journals Visual Sentiment Analysis on Social Media Data

2021 ◽  
pp. 366-372
Author(s):  
Harshala Bhoir ◽  
K. Jayamalini
Keyword(s):  
Social Media ◽  
Sentiment Analysis ◽  
State Of The Art ◽  
Social Media Data ◽  
Media Image ◽  
The Social ◽  
Textual Data ◽  
Art Works ◽  
Negative Sentiment ◽  
Media Data

Visual sentiment analysis is the way to automatically recognize positive and negative emotions from images, videos, graphics, stickers etc. To estimate the polarity of the sentiment evoked by images in terms of positive or negative sentiment, most of the state-of-the-art works exploit the text associated to a social post provided by the user. However, such textual data is typically noisy due to the subjectivity of the user which usually includes text useful to maximize the diffusion of the social post. Proposed system will extract and employ an Objective Text description of images automatically extracted from the visual content rather than the classic Subjective Text provided by the user. The proposed System will extract three views visual view, subjective text view and objective text view of social media image and will give sentiment polarity positive, negative or neutral based on hypothesis table.

scholarly journals Algorithmic Closeness in Mourning: Vernaculars of the Hashtag #grief on TikTok

2021 ◽  
Vol 7 (3) ◽  
pp. 205630512110423
Author(s):  
Moa Eriksson Krutrök
Keyword(s):  
Social Media ◽  
Content Analysis ◽  
Qualitative Content Analysis ◽  
Social Connections ◽  
Safe Spaces ◽  
Ranking Algorithms ◽  
Ranking Systems ◽  
The Social ◽  
Previous User ◽  
The Way

This study looks at how mourning is expressed using the hashtag #grief on the social media app TikTok using qualitative content analysis. In a dataset of 100 TikTok videos, this article explores how the TikTok ranking algorithms, which orders content based on previous user engagements, may connect people in mourning across the platform and how these platform-enabled interactions may shape grief expressions. The study shows how grief was narrated on TikTok, which sociotechnical templates (such as duets, stitches, and audios) were incorporated into such expressions, and how these expressions of grief challenged societal mourning norms. This article ends with a discussion about how different subcultural norms on TikTok are linked to the way in which ranking algorithms create social connections across the platform. This study proposes that the “algorithmic closeness” of TikTok users in grief allows them to challenge societal mourning norms in imagined safe spaces, shaped by the algorithmic ranking systems on the platform.

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