scholarly journals Over-Time Trends in Incivility on Social Media: Evidence From Political, Non-Political, and Mixed Sub-Reddits Over Eleven Years

2021 ◽  
Vol 3 ◽  
Author(s):  
Qiusi Sun ◽  
Magdalena Wojcieszak ◽  
Sam Davidson
Keyword(s):  
Social Media ◽  
Time Trends ◽  
Supervised Machine Learning ◽  
The Public ◽  
Online Interactions ◽  
The Past ◽  
Descriptive Account ◽  
Mixed Groups ◽  
Political Groups ◽  
Machine Learning Models

Incivility in social media has become a major concern of the public, who perceive uncivil online interactions to be both widespread and increasing. This study provides a descriptive account of incivility dynamics over the past 11 years by examining the trends of incivility in three main categories of social media interactions: political, mixed, and non-political. Using longitudinal data from Reddit that accounts for 95% of the entire Reddit universe across 11 years and relying on the combination of supervised machine learning models and traditional statistical inference, the study found that incivility consistently represents around 10% of total Reddit comments. Additionally, political groups tend to be more uncivil, and discussions in mixed groups that are not overtly political but nevertheless discuss politics are less uncivil than in political groups. We also found that the fluctuations of incivility correspond to offline events and platform-specific policies.

scholarly journals HOW SOCIAL MEDIA AFFORDS POPULIST POLITICS: REMARKS ON LIMINALITY BASED ON THE BRAZILIAN CASE

2020 ◽  
Vol 59 (1) ◽  
pp. 404-427
Author(s):  
Leticia Cesarino
Keyword(s):  
Social Media ◽  
Public Sphere ◽  
Discourse Markers ◽  
Online Ethnography ◽  
The Public ◽  
The Past ◽  
Making Sense ◽  
The Public Sphere ◽  
Key Aspects ◽  
And Linguistics

ABSTRACT In the past decade or so, populism and social media have been outstanding issues both in academia and the public sphere. At this point, evidence from multiple countries suggest that perceived parallels between the dynamics of social media and the mechanics of populist discourse may be more than just incidental, relating to a shared structural field. This article suggests one possible path towards making sense of how the dynamics of social media and the mechanics of populist mobilization have co-produced each other in the last decade or so. Navigating the interface between anthropology and linguistics, it takes key aspects of Victor Turner’s notion of liminality to suggest some of the ways in which social media’s anti-structural affordances may help lay a foundation for the contemporary flourishing of populist discourse: markers of social structure are suspended; communitas is formed; the culture core is addressed; mimesis and anti-structural inversions are performed; subjects become influenceable. I elaborate on this claim based on Brazilian materials, drawn from online ethnography on pro-Bolsonaro WhatsApp groups and other platforms such as Twitter and Facebook since 2018.

scholarly journals Microblog credibility indicators regarding misinformation of genetically modified food on Weibo

PLoS ONE ◽  
2021 ◽  
Vol 16 (6) ◽  
pp. e0252392
Author(s):  
Jiaojiao Ji ◽  
Naipeng Chao ◽  
Shitong Wei ◽  
George A. Barnett
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Science Communication ◽  
Predictive Power ◽  
Genetically Modified ◽  
Public Understanding ◽  
Supervised Machine Learning ◽  
Genetically Modified Food ◽  
The Public ◽  
Gm Food

The considerable amount of misinformation on social media regarding genetically modified (GM) food will not only hinder public understanding but also mislead the public to make unreasoned decisions. This study discovered a new mechanism of misinformation diffusion in the case of GM food and applied a framework of supervised machine learning to identify effective credibility indicators for the misinformation prediction of GM food. Main indicators are proposed, including user identities involved in spreading information, linguistic styles, and propagation dynamics. Results show that linguistic styles, including sentiment and topics, have the dominant predictive power. In addition, among the user identities, engagement, and extroversion are effective predictors, while reputation has almost no predictive power in this study. Finally, we provide strategies that readers should be aware of when assessing the credibility of online posts and suggest improvements that Weibo can use to avoid rumormongering and enhance the science communication of GM food.

scholarly journals Comparative Analysis of Deep Learning Techniques to detect Online Public Shaming

2021 ◽  
Vol 40 ◽  
pp. 03030
Author(s):  
Mehdi Surani ◽  
Ramchandra Mangrulkar
Keyword(s):  
Social Media ◽  
Deep Learning ◽  
Freedom Of Speech ◽  
Learning Approaches ◽  
Learning Models ◽  
Media Usage ◽  
The Past ◽  
Learning Techniques ◽  
Social Media Platforms ◽  
Machine Learning Models

Over the past years the exponential growth of social media usage has given the power to every individual to share their opinions freely. This has led to numerous threats allowing users to exploit their freedom of speech, thus spreading hateful comments, using abusive language, carrying out personal attacks, and sometimes even to the extent of cyberbullying. However, determining abusive content is not a difficult task and many social media platforms have solutions available already but at the same time, many are searching for more efficient ways and solutions to overcome this issue. Traditional models explore machine learning models to identify negative content posted on social media. Shaming categories are explored, and content is put in place according to the label. Such categorization is easy to detect as the contextual language used is direct. However, the use of irony to mock or convey contempt is also a part of public shaming and must be considered while categorizing the shaming labels. In this research paper, various shaming types, namely toxic, severe toxic, obscene, threat, insult, identity hate, and sarcasm are predicted using deep learning approaches like CNN and LSTM. These models have been studied along with traditional models to determine which model gives the most accurate results.

Building High Performance Explainable Machine Learning Models for Social Media-based Substance Use Prediction

2020 ◽  
Vol 29 (03n04) ◽  
pp. 2060009
Author(s):  
Tao Ding ◽  
Fatema Hasan ◽  
Warren K. Bickel ◽  
Shimei Pan
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Substance Use ◽  
Human Behavior ◽  
High Performance ◽  
Supervised Machine Learning ◽  
Learning Models ◽  
Wide Range ◽  
And Behavior ◽  
Machine Learning Models

Social media contain rich information that can be used to help understand human mind and behavior. Social media data, however, are mostly unstructured (e.g., text and image) and a large number of features may be needed to represent them (e.g., we may need millions of unigrams to represent social media texts). Moreover, accurately assessing human behavior is often difficult (e.g., assessing addiction may require medical diagnosis). As a result, the ground truth data needed to train a supervised human behavior model are often difficult to obtain at a large scale. To avoid overfitting, many state-of-the-art behavior models employ sophisticated unsupervised or self-supervised machine learning methods to leverage a large amount of unsupervised data for both feature learning and dimension reduction. Unfortunately, despite their high performance, these advanced machine learning models often rely on latent features that are hard to explain. Since understanding the knowledge captured in these models is important to behavior scientists and public health providers, we explore new methods to build machine learning models that are not only accurate but also interpretable. We evaluate the effectiveness of the proposed methods in predicting Substance Use Disorders (SUD). We believe the methods we proposed are general and applicable to a wide range of data-driven human trait and behavior analysis applications.

scholarly journals The Effects of Social Media and Hashtag Activism on Enhancing Student Voice to Create Institutional Curriculum Changes

2019 ◽  
Vol 4 ◽  
Author(s):  
Michelle Nguyen
Keyword(s):  
Social Media ◽  
Core Curriculum ◽  
Christian University ◽  
The Public ◽  
Curriculum Changes ◽  
The Past ◽  
The People ◽  
Social Protests ◽  
Equity And Inclusion ◽  
Made In

College-aged students are often at the forefront of social movements. Students are commonly the people who use their voices to fight for changes at the institutions of higher education that they are attending. Various social media outlets, specifically Twitter, have allowed these students to organize social protests online through hashtag activism. Hashtag activism allows individuals to connect to other individuals protesting for a similar cause through a common hashtagged word or phrase. I have been an undergraduate student at Texas Christian University (TCU) for the past four years, and I have seen the ways in which hashtag activism has laid the foundation for institutional changes, particularly in the curriculum, to be made. In this thesis, I examine how students who participated in the hashtag movements #BeingMinorityAtTCU and/or #DearTCU were able to show the TCU administration glimpses of their personal stories through the public venue of social media, creating a pressure that led TCU administration, faculty, and staff to shift the core curriculum to include a Diversity, Equity, and Inclusion (DEI) overlay. This overlay requires students to take a DEI-focused course that will encourage conversations about identity and how to be a leader in this diverse country and world that we live in. 

The Media Event as Enhanced News Story

2019 ◽  
pp. 781-790
Author(s):  
Andrew Fox
Keyword(s):  
Social Media ◽  
Final Conclusion ◽  
News Story ◽  
News Production ◽  
The Public ◽  
News Stories ◽  
The Past ◽  
The Media ◽  
Use Of Social Media ◽  
Media Event

Social media has, over the past decade, become a significant method of communication. People can now interact with each other more easily and more frequently than in the past thanks to websites like Facebook, Twitter and Instagram. This chapter concerns itself with examining how social media has enabled the public and the news broadcasters to work more closely together. Explored are three key elements. Firstly, there is a review of literature which discusses issues of convergence and the changing nature of news production. Secondly, three major news stories from 2015 act as case studies to discuss how the public contributed to the “eventisation” of the stories through the use of social media platforms. These analytical elements of the chapter feed into the broader context, which is how a media event is now defined given the changing nature of the public's role in news production. The chapter concludes by offering an explanation as to how a media event can now be potentially driven by the public's interaction with the news organisations through social media. Therefore the overarching conclusion that is reached is that the media event as defined in the traditional sense (a live broadcast) has been superseded by 24 hour rolling news channels constant live coverage of news events and that the broadcasters are increasingly reliant on a public contribution. We now have a middle tier between a traditional news story and a media event, the enhanced news story. The final conclusion of the chapter is that it is possible that an enhanced news story can easily become a media event but we need to be cautious not to be seen to be “over eventising” some stories for the sake of filling schedules.

The Media Event as Enhanced News Story

2016 ◽  
pp. 17-27 ◽  
Author(s):  
Andrew Fox
Keyword(s):  
Social Media ◽  
Final Conclusion ◽  
News Story ◽  
News Production ◽  
The Public ◽  
News Stories ◽  
The Past ◽  
The Media ◽  
Use Of Social Media ◽  
Media Event

Social media has, over the past decade, become a significant method of communication. People can now interact with each other more easily and more frequently than in the past thanks to websites like Facebook, Twitter and Instagram. This chapter concerns itself with examining how social media has enabled the public and the news broadcasters to work more closely together. Explored are three key elements. Firstly, there is a review of literature which discusses issues of convergence and the changing nature of news production. Secondly, three major news stories from 2015 act as case studies to discuss how the public contributed to the “eventisation” of the stories through the use of social media platforms. These analytical elements of the chapter feed into the broader context, which is how a media event is now defined given the changing nature of the public's role in news production. The chapter concludes by offering an explanation as to how a media event can now be potentially driven by the public's interaction with the news organisations through social media. Therefore the overarching conclusion that is reached is that the media event as defined in the traditional sense (a live broadcast) has been superseded by 24 hour rolling news channels constant live coverage of news events and that the broadcasters are increasingly reliant on a public contribution. We now have a middle tier between a traditional news story and a media event, the enhanced news story. The final conclusion of the chapter is that it is possible that an enhanced news story can easily become a media event but we need to be cautious not to be seen to be “over eventising” some stories for the sake of filling schedules.

scholarly journals The Whiteness of Redmen: Indigenous Mascots, Social media and an Antiracist Intervention

2017 ◽  
Vol 21 ◽  
Author(s):  
Sheelah McLean ◽  
Alex Wilson ◽  
Erica Lee
Keyword(s):  
Social Media ◽  
Media Campaign ◽  
High School Sports ◽  
The Public ◽  
The Past ◽  
School Sports ◽  
The Social ◽  
Indigenous Movement ◽  
White Settlers ◽  
And Control

Resistance to the use of Indigenous themed mascots in North America has taken a variety of forms over the past several decades. This paper describes and analyses how a new vehicle for resistance, social media, can be integral to dismantling and eradicating racist images of Indigenous peoples. Specifically, this paper focusses on one campaign that questioned a high school sports mascot and team named the “Redmen”. By using examples from social media, the authors demonstrate how White settlers came to rely on the mascot imagery as a way to position themselves as superior and to regulate representations of Indigeneity. The authors’ analysis posits that the mascot is in itself a form of racialised colonial violence and they discuss how the name and mascot were protected by and through white settler surveillance and control. To intervene in this discourse of superiority and regulation, the paper describes how an anti-racist approach was used to design a social media campaign that built mass critical consciousness and a network of support within the community. The social media campaign coincided with and rallied support from the grassroots Indigenous Movement, Idle No More. The larger joint effort strategically and effectively redirected the public and critical focus to how the “Redmen” name and logo and other racist Indigenous mascots become normalised. Increased knowledge via social media catalysed a shift in public opinion which ultimately leads to retirement of the team name, logo and mascot.

scholarly journals Text Polarity Detection using Multiple Supervised Machine Learning Algorithms

2020 ◽  
Vol 9 (3) ◽  
pp. 1612-1618
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Sentiment Analysis ◽  
Large Scale ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Supervised Machine Learning ◽  
The Public ◽  
Social Media Platforms ◽  
Day By Day

Sentiment analysis is the classifying of a review, opinion or a statement into categories, which brings clarity about specific sentiments of customers or the concerned group to businesses and developers. These categorized data are very critical to the development of businesses and understanding the public opinion. The need for accurate opinion and large-scale sentiment analysis on social media platforms is growing day by day. In this paper, a number of machine learning algorithms are trained and applied on twitter datasets and their respective accuracies are determined separately on different polarities of data, thereby giving a glimpse to which algorithm works best and which works worst..

Exploring the Relationship Between Campaign Discourse on Facebook and the Public’s Comments: A Case Study of Incivility During the 2016 US Presidential Election

2020 ◽  
pp. 003232171989081
Author(s):  
Patrícia Rossini ◽  
Jennifer Stromer-Galley ◽  
Feifei Zhang
Keyword(s):  
Social Media ◽  
Public Discourse ◽  
Political Campaigns ◽  
Supervised Machine Learning ◽  
Presidential Debates ◽  
The Public ◽  
The Us ◽  
Negative Messages ◽  
The Relationship

Social media is now ubiquitously used by political campaigns, but less attention has been given to public discussions that take place on candidates’ free public accounts on social media. Also unclear is whether there is a relationship between campaign messaging and the tone of public comments. To address this gap, this article analyzes public comments on Facebook accounts of candidates Trump and Clinton during the US election presidential debates in 2016. We hypothesize that attack messages posted by the candidates predict uncivil reactions by the public and that the public is more likely to be uncivil when attacking candidates. We use content analysis, supervised machine learning, and text mining to analyze candidates’ posts and public comments. Our results suggest that Clinton was the target of substantially more uncivil comments. Negative messages by the candidates are not associated with incivility by the public, but comments are significantly more likely to be uncivil when the public is attacking candidates. These results suggest that the public discourse around political campaigns might be less affected by what campaigns post on social media than by the public’s own perceptions and feelings toward the candidates.

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