Folk classification of social media platforms: Preliminary findings

2016 ◽  
Author(s):  
Gil Wilkes ◽  
Jaigris Hodson ◽  
Brian Traynor
Keyword(s):  
Social Media ◽  
Folk Classification ◽  
Social Media Platforms

A taxonomy of value co-creation on Weibo – a communication perspective

2018 ◽  
Vol 30 (4) ◽  
pp. 2075-2092 ◽  
Author(s):  
Jing Ge ◽  
Ulrike Gretzel
Keyword(s):  
Social Media ◽  
Future Research ◽  
Marketing Organization ◽  
Content Type ◽  
Social Media Platform ◽  
Social Media Platforms ◽  
Media Platform ◽  
Comprehensive Classification ◽  
Practical Implications

Purpose This paper aims to develop a taxonomy of value co-creation types occurring in firm-customer interactions on social media. Design/methodology/approach In total, 570 destination marketing organization (DMO)-initiated posts on Weibo and 3,137 responses were collected to develop a taxonomy by conducting qualitative empirical-to-conceptual analysis. To apply the taxonomy through conceptual-to-empirical analysis, 100 DMO-initiated posts and 823 responses were collected. Findings The communication-focused value co-creation taxonomy shows a variety of co-creators, verbal and non-verbal communicative co-creation actions facilitated by social media, and different co-created value types. Research limitations/implications This study used a single social media platform and selected three DMOs’ Weibo accounts. Future research should focus on other types of firms and different social media platforms. Practical implications This study used a single social media platform and selected three DMOs’ Weibo accounts. Future research should focus on other types of firms and different social media platforms. Originality/value This study enriches the tourism literature and the general marketing literature by examining value co-creation from a communication perspective and provides a comprehensive classification of value co-creation opportunities on social media.

scholarly journals CRITICAL SIMULATION: INVESTIGATING THE WORK OF MACHINE VISION IN VISUAL SOCIAL MEDIA CULTURE

2019 ◽  
Vol 2019 ◽  
Author(s):  
Nicholas Carah ◽  
Daniel Angus ◽  
Adam Smith
Keyword(s):  
Social Media ◽  
Machine Vision ◽  
Business Models ◽  
Participatory Culture ◽  
Media Culture ◽  
Commercial Application ◽  
Music Festival ◽  
Social Media Platforms ◽  
Visual Cultures

Social media are in the midst of an emphatic visual turn (Highfield & Leaver 2016), characterised by the convergence of everyday visual expression with professional creative practice and advertising. The advertising-driven business models of social media platforms increasingly depend on automation. Platforms’ use of machine vision is a key frontier in the algorithmic classification of culture. Machine vision algorithms automatically classify and misclassify faces, expressions, objects, and brand logos in the images users create and share. Images shared by platform users form vast databases used to train these same algorithms. Despite widespread use by social platforms, machine vision is poorly understood and accounted for in the study of everyday visual cultures. In this paper we detail a critical response to the use of automation in visual social media, called critical simulation. We outline a critical simulation framework, the ‘Image Machine’, focussed firstly on Instagram. The Image Machine comprises an Instagram data harvester, and open-access machine vision toolbox that allows digital humanities researchers to interrogate the inner workings of these algorithms and analyse their visual (mis)classifications. In this paper we showcase results from the Image Machine applied to images emanating from a major Australian music festival, Splendour in the Grass. This case examines not only how machine vision classifies and operates on culture, but also how these techniques are being operationalised within the advertising model and promotional culture of platforms like Instagram. We argue that the commercial application of machine vision is interdependent with the participatory culture of platforms like Instagram.

scholarly journals Sentiment Analysis for Detecting Cyberbullying Using TF-IDF and SVM

2020 ◽  
Vol 4 (6) ◽  
Author(s):  
Wahyu Adi Prabowo ◽  
Fitriani Azizah
Keyword(s):  
Social Media ◽  
System Model ◽  
Support Vector ◽  
Inverse Document Frequency ◽  
Document Frequency ◽  
Digital Development ◽  
Social Media Platforms ◽  
Python Programming ◽  
System Model Design

Social media has become a new method of today’s communication in a new digitalize era. Children and adults have used social media a lot in interacting with others. Therefore social media has shifted conventional communication into digital one. This digital development on social media is a serious problem that must be faced because it has been found that there are more and more acts of cyberbullying. This act of cyberbullying can attack the psychic, causing depression up to suicide. The dangers of cyberbullying are troubling and cause concern to the community. Therefore, this study will analyze the sentiment on the comments contained on social media to find out the value of sentiment from comments on social media platforms. The comment data will be processed at the preprocessing stage, Term Frequency-Inverse Document Frequency (TF-IDF), and the Support Vector Machine (SVM) classification method. Comment data to be classified as 1500 data taken using crawling data through libraries in python programming and divided into 80% data training and 20% data testing. Based on the results of the test, the accuracy value is 93%, the precision value is 95%, and the recall value is 97%. In this research, a system model design is also carried out where the system can be integrated with the browser to open a user page on the classification of comments that have been input into the system.

scholarly journals Regulatory approaches to social media

2020 ◽  
Vol 338 ◽  
pp. 337-346
Author(s):  
Balázs Bartóki-Gönczy
Keyword(s):  
Social Media ◽  
Service Provider ◽  
Hate Speech ◽  
National Level ◽  
State Intervention ◽  
Critical Approach ◽  
Private Regulation ◽  
Social Media Platforms ◽  
Media Platform

Social media platforms are mainly characterised by private regulation.2 However, their direct and indirect impact on society has become such (fake news, hate speech, incitement to terrorism, data protection breaches, impact on the viability of professional journalism) that private regulatory mechanisms in place (often opaque and not transparent) seem to be inadequate. In my presentation, I would first address the problem of legal classification of these services (media service provider vs. intermediary service provider), since the answer to this question is a prerequisite for any state intervention. I would then present the regulatory initiatives (with a critical approach) at EU and national level which might shape the future of ’social media platform’ regulation.

scholarly journals Using Machine Learning–Based Approaches for the Detection and Classification of Human Papillomavirus Vaccine Misinformation: Infodemiology Study of Reddit Discussions

10.2196/26478 ◽  
2021 ◽  
Vol 23 (8) ◽  
pp. e26478
Author(s):  
Jingcheng Du ◽  
Sharice Preston ◽  
Hanxiao Sun ◽  
Ross Shegog ◽  
Rachel Cunningham ◽  
...  
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Social Media ◽  
Human Papillomavirus ◽  
Convolutional Neural Network ◽  
Hpv Vaccine ◽  
Support Vector ◽  
Social Media Platforms ◽  
Vaccine Promotion

Background The rapid growth of social media as an information channel has made it possible to quickly spread inaccurate or false vaccine information, thus creating obstacles for vaccine promotion. Objective The aim of this study is to develop and evaluate an intelligent automated protocol for identifying and classifying human papillomavirus (HPV) vaccine misinformation on social media using machine learning (ML)–based methods. Methods Reddit posts (from 2007 to 2017, N=28,121) that contained keywords related to HPV vaccination were compiled. A random subset (2200/28,121, 7.82%) was manually labeled for misinformation and served as the gold standard corpus for evaluation. A total of 5 ML-based algorithms, including a support vector machine, logistic regression, extremely randomized trees, a convolutional neural network, and a recurrent neural network designed to identify vaccine misinformation, were evaluated for identification performance. Topic modeling was applied to identify the major categories associated with HPV vaccine misinformation. Results A convolutional neural network model achieved the highest area under the receiver operating characteristic curve of 0.7943. Of the 28,121 Reddit posts, 7207 (25.63%) were classified as vaccine misinformation, with discussions about general safety issues identified as the leading type of misinformed posts (2666/7207, 36.99%). Conclusions ML-based approaches are effective in the identification and classification of HPV vaccine misinformation on Reddit and may be generalizable to other social media platforms. ML-based methods may provide the capacity and utility to meet the challenge involved in intelligent automated monitoring and classification of public health misinformation on social media platforms. The timely identification of vaccine misinformation on the internet is the first step in misinformation correction and vaccine promotion.

scholarly journals Fake News Detection Using Machine Learning Ensemble Methods

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-11
Author(s):  
Iftikhar Ahmad ◽  
Muhammad Yousaf ◽  
Suhail Yousaf ◽  
Muhammad Ovais Ahmad
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Information Dissemination ◽  
Ensemble Methods ◽  
Machine Learning Algorithms ◽  
Superior Performance ◽  
Automated Classification ◽  
Social Media Platforms ◽  
Real World Datasets

The advent of the World Wide Web and the rapid adoption of social media platforms (such as Facebook and Twitter) paved the way for information dissemination that has never been witnessed in the human history before. With the current usage of social media platforms, consumers are creating and sharing more information than ever before, some of which are misleading with no relevance to reality. Automated classification of a text article as misinformation or disinformation is a challenging task. Even an expert in a particular domain has to explore multiple aspects before giving a verdict on the truthfulness of an article. In this work, we propose to use machine learning ensemble approach for automated classification of news articles. Our study explores different textual properties that can be used to distinguish fake contents from real. By using those properties, we train a combination of different machine learning algorithms using various ensemble methods and evaluate their performance on 4 real world datasets. Experimental evaluation confirms the superior performance of our proposed ensemble learner approach in comparison to individual learners.

MOBILE SOCIAL MEDIA

2018 ◽  
Vol 8 (3) ◽  
pp. 8
Author(s):  
PHILIP ADEBO
Keyword(s):  
Social Media ◽  
Mobile Apps ◽  
Social Revolution ◽  
Social Media Platforms ◽  
The Way

The emergence of mobile connectivity is revolutionizing the way people live, work, interact, and socialize. Mobile social media is the heart of this social revolution. It is becoming a global phenomenon as it enables IP-connectivity for people on the move. Popular social media platforms such as Facebook, Twitter, and MySpace have made mobile apps for their users to have instant access from anywhere at any time. This paper provides a brief introduction into mobile social media, their benefits, and challenges.

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