Playing Politics: How Sabarimala Played Out on TikTok

2021 ◽  
pp. 000276422198976
Author(s):  
Darsana Vijay ◽  
Alex Gekker
Keyword(s):  
Popular Culture ◽  
Political Participation ◽  
Hate Speech ◽  
News Coverage ◽  
Political Actor ◽  
The Past ◽  
Indian Context ◽  
Political Contention ◽  
Social Media Platforms ◽  
Potential Strategy

TikTok is commonly known as a playful, silly platform where teenagers share 15-second videos of crazy stunts or act out funny snippets from popular culture. In the past few years, it has experienced exponential growth and popularity, unseating Facebook as the most downloaded app. Interestingly, recent news coverage notes the emergence of TikTok as a political actor in the Indian context. They raise concerns over the abundance of divisive content, hate speech, and the lack of platform accountability in countering these issues. In this article, we analyze how politics is performed on TikTok and how the platform’s design shapes such expressions and their circulation. What does the playful architecture of TikTok mean to the nature of its political discourse and participation? To answer this, we review existing academic work on play, media, and political participation and then examine the case of Sabarimala through the double lens of ludic engagement and platform-specific features. The efficacy of play as a productive heuristic to study political contention on social media platforms is demonstrated. Finally, we turn to ludo-literacy as a potential strategy that can reveal the structures that order playful political participation and can initiate alternative modes of playing politics.

scholarly journals TUNING OUT HATE SPEECH ON REDDIT: AUTOMATING MODERATION AND DETECTING TOXICITY IN THE MANOSPHERE

2020 ◽  
Author(s):  
Verity Trott ◽  
Jennifer Beckett ◽  
Venessa Paech
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Hate Speech ◽  
The Past ◽  
Community Platforms ◽  
Social Media Platforms ◽  
Automated Machine Learning ◽  
Tuning Out

Over the past two years social media platforms have been struggling to moderate at scale. At the same time, they have come under fire for failing to mitigate the risks of perceived ‘toxic’ content or behaviour on their platforms. In effort to better cope with content moderation, to combat hate speech, ‘dangerous organisations’ and other bad actors present on platforms, discussion has turned to the role that automated machine-learning (ML) tools might play. This paper contributes to thinking about the role and suitability of ML for content moderation on community platforms such as Reddit and Facebook. In particular, it looks at how ML tools operate (or fail to operate) effectively at the intersection between online sentiment within communities and social and platform expectations of acceptable discourse. Through an examination of the r/MGTOW subreddit we problematise current understandings of the notion of ‘tox¬icity’ as applied to cultural or social sub-communities online and explain how this interacts with Google’s Perspective tool.

Sophisticated Hate Stratagems: Unpacking the Era of Distrust

2021 ◽  
pp. 000276422110050
Author(s):  
Rita Kirk ◽  
Dan Schill
Keyword(s):  
Social Media ◽  
Hate Speech ◽  
Body Politic ◽  
Democratic Governance ◽  
The Body ◽  
Linguistic Expression ◽  
The Past ◽  
Social Media Platforms ◽  
Democratic Rule ◽  
Points Of View

Over the past decade, the rise of political extremism and its associated linguistic expression resulted in communication companies’ decisions to restrict hate speech and, in many cases, ban speech emanating from specific users. Before we attempt to regulate expression per se—whether through “cancelling” expression, “deplatforming” speakers through suspensions or platform restrictions, rewriting social media terms of service, or criminalizing harmful speech—we should seek a clearer understanding of how hate appeals are used to accomplish particular communication purposes. In this analysis, we analyze hate speech as a stratagem—an artifice or trick of war—used with great effect during the 2020 election. Our concern is how this tactic is used to harm the body politic, reducing citizen ability to engage with divergent publics and points of view, and threatening democratic rule. Critically, we must understand how communication on social media platforms is being used to destabilize the communication environment and prevent the robust discussion of ideas in a public forum, a prerequisite for democratic governance.

Obesity and adiposity: the culprit of dietary protein efficacy

2020 ◽  
Vol 134 (4) ◽  
pp. 389-401
Author(s):  
Carla El-Mallah ◽  
Omar Obeid
Keyword(s):  
Food Intake ◽  
Behavioural Change ◽  
Physiological Mechanism ◽  
Short Term ◽  
The Past ◽  
Body Adiposity ◽  
Reduce Sugar ◽  
Eye Opening ◽  
Potential Strategy ◽  
Limitations And Exceptions

Abstract Obesity and increased body adiposity have been alarmingly increasing over the past decades and have been linked to a rise in food intake. Many dietary restrictive approaches aiming at reducing weight have resulted in contradictory results. Additionally, some policies to reduce sugar or fat intake were not able to decrease the surge of obesity. This suggests that food intake is controlled by a physiological mechanism and that any behavioural change only leads to a short-term success. Several hypotheses have been postulated, and many of them have been rejected due to some limitations and exceptions. The present review aims at presenting a new theory behind the regulation of energy intake, therefore providing an eye-opening field for energy balance and a potential strategy for obesity management.

scholarly journals Quantified Nostalgia: Social Media, Metrics, and Memory

2021 ◽  
Vol 7 (2) ◽  
pp. 205630512110088
Author(s):  
Benjamin N. Jacobsen ◽  
David Beer
Keyword(s):  
Social Media ◽  
Focus Group ◽  
Qualitative Interviews ◽  
The Past ◽  
Group Data ◽  
Focus Group Data ◽  
Social Media Platforms ◽  
Memory Practices ◽  
The Relationship

As social media platforms have developed over the past decade, they are no longer simply sites for interactions and networked sociality; they also now facilitate backwards glances to previous times, moments, and events. Users’ past content is turned into definable objects that can be scored, rated, and resurfaced as “memories.” There is, then, a need to understand how metrics have come to shape digital and social media memory practices, and how the relationship between memory, data, and metrics can be further understood. This article seeks to outline some of the relations between social media, metrics, and memory. It examines how metrics shape remembrance of the past within social media. Drawing on qualitative interviews as well as focus group data, the article examines the ways in which metrics are implicated in memory making and memory practices. This article explores the effect of social media “likes” on people’s memory attachments and emotional associations with the past. The article then examines how memory features incentivize users to keep remembering through accumulation. It also examines how numerating engagements leads to a sense of competition in how the digital past is approached and experienced. Finally, the article explores the tensions that arise in quantifying people’s engagements with their memories. This article proposes the notion of quantified nostalgia in order to examine how metrics are variously performative in memory making, and how regimes of ordinary measures can figure in the engagement and reconstruction of the digital past in multiple ways.

scholarly journals How Popular Culture Prompts Youth Collective Political Expression and Cross-Cutting Political Talk on Social Media: A Cross-Platform Analysis

2021 ◽  
Vol 7 (2) ◽  
pp. 205630512110088
Author(s):  
Ioana Literat ◽  
Neta Kligler-Vilenchik
Keyword(s):  
Social Media ◽  
Popular Culture ◽  
Qualitative Content Analysis ◽  
Political Expression ◽  
Political Views ◽  
Political Talk ◽  
Social Media Platforms ◽  
Cross Platform ◽  
Symbolic Resource ◽  
Political Differences

Adopting a comparative cross-platform approach, we examine youth political expression and conversation on social media, as prompted by popular culture. Tracking a common case study—the practice of building Donald Trump’s border wall within the videogame Fortnite—across three social media platforms popular with youth (YouTube, TikTok, Instagram), we ask: How do popular culture artifacts prompt youth political expression, as well as cross-cutting political talk with those holding different political views, across social media platforms? A mixed methods approach, combining quantitative and qualitative content analysis of around 6,400 comments posted on relevant artifacts, illuminates youth popular culture as a shared symbolic resource that stimulates communication within and across political differences—although, as our findings show, it is often deployed in a disparaging manner. This cross-platform analysis, grounded in contemporary youth culture and sociopolitical dynamics, enables a deeper understanding of the interplay between popular culture, cross-cutting political talk, and the role that different social media platforms play in shaping these expressive practices.

scholarly journals Emotionally Informed Hate Speech Detection: A Multi-target Perspective

2021 ◽  
Author(s):  
Patricia Chiril ◽  
Endang Wahyu Pamungkas ◽  
Farah Benamara ◽  
Véronique Moriceau ◽  
Viviana Patti
Keyword(s):  
Hate Speech ◽  
Binary Classification ◽  
Online Communication ◽  
Vulnerable Groups ◽  
Speech Detection ◽  
Social Media Platforms ◽  
Sentic Computing ◽  
Specific Manifestation ◽  
The Impact ◽  
First Time

AbstractHate Speech and harassment are widespread in online communication, due to users' freedom and anonymity and the lack of regulation provided by social media platforms. Hate speech is topically focused (misogyny, sexism, racism, xenophobia, homophobia, etc.), and each specific manifestation of hate speech targets different vulnerable groups based on characteristics such as gender (misogyny, sexism), ethnicity, race, religion (xenophobia, racism, Islamophobia), sexual orientation (homophobia), and so on. Most automatic hate speech detection approaches cast the problem into a binary classification task without addressing either the topical focus or the target-oriented nature of hate speech. In this paper, we propose to tackle, for the first time, hate speech detection from a multi-target perspective. We leverage manually annotated datasets, to investigate the problem of transferring knowledge from different datasets with different topical focuses and targets. Our contribution is threefold: (1) we explore the ability of hate speech detection models to capture common properties from topic-generic datasets and transfer this knowledge to recognize specific manifestations of hate speech; (2) we experiment with the development of models to detect both topics (racism, xenophobia, sexism, misogyny) and hate speech targets, going beyond standard binary classification, to investigate how to detect hate speech at a finer level of granularity and how to transfer knowledge across different topics and targets; and (3) we study the impact of affective knowledge encoded in sentic computing resources (SenticNet, EmoSenticNet) and in semantically structured hate lexicons (HurtLex) in determining specific manifestations of hate speech. We experimented with different neural models including multitask approaches. Our study shows that: (1) training a model on a combination of several (training sets from several) topic-specific datasets is more effective than training a model on a topic-generic dataset; (2) the multi-task approach outperforms a single-task model when detecting both the hatefulness of a tweet and its topical focus in the context of a multi-label classification approach; and (3) the models incorporating EmoSenticNet emotions, the first level emotions of SenticNet, a blend of SenticNet and EmoSenticNet emotions or affective features based on Hurtlex, obtained the best results. Our results demonstrate that multi-target hate speech detection from existing datasets is feasible, which is a first step towards hate speech detection for a specific topic/target when dedicated annotated data are missing. Moreover, we prove that domain-independent affective knowledge, injected into our models, helps finer-grained hate speech detection.

scholarly journals Social Media Toxicity Classification Using Deep Learning: Real-World Application UK Brexit

Electronics ◽  
2021 ◽  
Vol 10 (11) ◽  
pp. 1332
Author(s):  
Hong Fan ◽  
Wu Du ◽  
Abdelghani Dahou ◽  
Ahmed A. Ewees ◽  
Dalia Yousri ◽  
...  
Keyword(s):  
Social Media ◽  
Hate Speech ◽  
Modern Society ◽  
User Generated Content ◽  
Real World Application ◽  
Proposed Model ◽  
Social Media Platforms ◽  
Public Dataset ◽  
The Uk ◽  
Harmful Side Effect

Social media has become an essential facet of modern society, wherein people share their opinions on a wide variety of topics. Social media is quickly becoming indispensable for a majority of people, and many cases of social media addiction have been documented. Social media platforms such as Twitter have demonstrated over the years the value they provide, such as connecting people from all over the world with different backgrounds. However, they have also shown harmful side effects that can have serious consequences. One such harmful side effect of social media is the immense toxicity that can be found in various discussions. The word toxic has become synonymous with online hate speech, internet trolling, and sometimes outrage culture. In this study, we build an efficient model to detect and classify toxicity in social media from user-generated content using the Bidirectional Encoder Representations from Transformers (BERT). The BERT pre-trained model and three of its variants has been fine-tuned on a well-known labeled toxic comment dataset, Kaggle public dataset (Toxic Comment Classification Challenge). Moreover, we test the proposed models with two datasets collected from Twitter from two different periods to detect toxicity in user-generated content (tweets) using hashtages belonging to the UK Brexit. The results showed that the proposed model can efficiently classify and analyze toxic tweets.

scholarly journals Nostalgic transmediation: A not-so-final fantasy? Ichigo’s Sheet Music online platform as an object network of creative practice

2020 ◽  
Vol 9 (2) ◽  
pp. 179-193
Author(s):  
Ruth Barratt-Peacock ◽  
Sophia Staite
Keyword(s):  
Popular Culture ◽  
Social Practice ◽  
Sheet Music ◽  
Online Platform ◽  
Creative Practice ◽  
Music Transcription ◽  
The Past ◽  
Social Nature

Using the music of the Final Fantasy game series as our case study, we follow the music through processes of transmediation in two very different contexts: the Netflix series Dad of Light and music transcription forum Ichigo’s Sheet Music. We argue that these examples reveal transmediation acting as a process of ‘emptying’, allowing the music to carry its nostalgic cargo of affect into new relationships and contexts. This study’s theoretical combination of transmediation with Bainbridge’s object networks of social practice frame challenges normative definitions of nostalgia. The phenomenon of ‘emptying’ we identify reveals a function of popular culture nostalgia that differs from the dominant understanding as a triggering of generalized emotional longing for (or the desire to return to) the past. Instead, this article uncovers a nostalgia that is defined by personal and communal creative engagement and highlights the active and social nature of transmediated popular culture nostalgia.

Gaining a Past, Losing a Future: Web 2.0 and Internet Historicity

2012 ◽  
Vol 143 (1) ◽  
pp. 99-109 ◽  
Author(s):  
Matthew Allen
Keyword(s):  
Popular Culture ◽  
Web 2.0 ◽  
First Century ◽  
The Internet ◽  
Historical Interpretation ◽  
Web 3.0 ◽  
The Past ◽  
The Future ◽  
Or Practice ◽  
The Web

This article explore how, in the first decade of the twenty-first century, the internet became historicised, meaning that its public existence is now explicitly framed through a narrative that locates the current internet in relation to a past internet. Up until this time, in popular culture, the internet had been understood mainly as the future-in-the-present, as if it had no past. The internet might have had a history, but it had no historicity. That has changed because of Web 2.0, and the effects of Tim O'Reilly's creative marketing of that label. Web 2.0, in this sense not a technology or practice but the marker of a discourse of historical interpretation dependent on versions, created for us a second version of the web, different from (and yet connected to) that of the 1990s. This historicising moment aligned the past and future in ways suitable to those who might control or manage the present. And while Web 3.0, implied or real, suggests the ‘future’, it also marks out a loss of other times, or the possibility of alterity understood through temporality.

scholarly journals Pamięć w kulturze teraźniejszości

2016 ◽  
Vol 60 (4) ◽  
pp. 121-141
Author(s):  
Elżbieta Tarkowska
Keyword(s):  
Popular Culture ◽  
New Media ◽  
Collective Memory ◽  
Communication Technologies ◽  
Future Research ◽  
Contemporary Culture ◽  
The Past ◽  
Cultural Phenomenon ◽  
Social Time ◽  
Media And Communication

One of the most substantial interdisciplinary topics in the study of contemporary culture is change in social time, which is expressed in the compression of time (and space) and changing relationships between the past, present, and future. Research and analysis situate the present in an exceptional position in contemporary culture, providing us with the term ‘culture of the present.’ At the same time, however, we are dealing with a phenomenon labeled the ‘explosion of memory’—an astounding multidirectional and multifaceted rise in interest in the past. It is therefore worthwhile to investigate the structures and mechanisms of collective memory, as well as how the past is defined in contemporary culture, from the perspective of time as a social and cultural phenomenon. Questions should be asked regarding the mechanisms that unite the dominance of the present in culture with a rising interest in the past. The perspective of social time reveals that the ‘culture of the present,’ the current dominating forms of memory intensification, and the heightened awareness of the past, are influenced by the same or similar factors. These include new media and communication technologies, as well as consumption and popular culture, which change the structure of time, condense the time horizon, alter the manner in which the past is experienced, and modify the mechanisms of collective memory.

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