scholarly journals NOT EVEN THE SKY IS THE LIMIT: THE MEANINGS OF CONSUMPTION AND THE DYNAMICS OF SOCIAL MOBILITY ON THE @blogueiradebaixarenda PROFILE ON INSTAGRAM AND YOUTUBE

2020 ◽  
Vol 10 (3) ◽  
pp. 831-859
Author(s):  
Carla Barros
Keyword(s):  
Social Networks ◽  
Social Media ◽  
Social Mobility ◽  
Low Income ◽  
Online Social Networks ◽  
Social Dynamics ◽  
Observational Research ◽  
Cultural Mediator ◽  
Consumption Practices ◽  
Social Media Platforms

Abstract The article sets out to explore the meanings surrounding consumption on the @blogueiradebaixarenda profile on the Instagram and YouTube online social networks, considering the perceptions of materiality and their articulations with the dynamics of social mobility. It analyses the elements making up the “low-income lifestyle” as a native category within the context of “digital influencers.” Through online observational research, the posts, hashtags and comments on both social media platforms were analysed, seeking to explore how consumption practices appear as mediators of social dynamics and identity constructs. Among the results, the articulations between materiality and social mobility, the idea of minimalism within the “low-income lifestyle” and the blogger’s status as a cultural mediator are highlighted.

Fuzzy based feature engineering architecture for sentiment analysis of medical discussion over online social networks

2021 ◽  
pp. 1-13
Author(s):  
C S Pavan Kumar ◽  
L D Dhinesh Babu
Keyword(s):  
Machine Learning ◽  
Social Networks ◽  
Social Media ◽  
Sentiment Analysis ◽  
Membership Function ◽  
Online Social Networks ◽  
Learning Model ◽  
Feature Engineering ◽  
Machine Learning Model ◽  
Social Media Platforms

Sentiment analysis is widely used to retrieve the hidden sentiments in medical discussions over Online Social Networking platforms such as Twitter, Facebook, Instagram. People often tend to convey their feelings concerning their medical problems over social media platforms. Practitioners and health care workers have started to observe these discussions to assess the impact of health-related issues among the people. This helps in providing better care to improve the quality of life. Dementia is a serious disease in western countries like the United States of America and the United Kingdom, and the respective governments are providing facilities to the affected people. There is much chatter over social media platforms concerning the patients’ care, healthy measures to be followed to avoid disease, check early indications. These chatters have to be carefully monitored to help the officials take necessary precautions for the betterment of the affected. A novel Feature engineering architecture that involves feature-split for sentiment analysis of medical chatter over online social networks with the pipeline is proposed that can be used on any Machine Learning model. The proposed model used the fuzzy membership function in refining the outputs. The machine learning model has obtained sentiment score is subjected to fuzzification and defuzzification by using the trapezoid membership function and center of sums method, respectively. Three datasets are considered for comparison of the proposed and the regular model. The proposed approach delivered better results than the normal approach and is proved to be an effective approach for sentiment analysis of medical discussions over online social networks.

scholarly journals Turn Threats into “Treatments”? Social Bots’ Possible Applications in Health Communication during Pandemics

2021 ◽  
Vol 2 (2) ◽  
pp. 281-288
Author(s):  
Zhenling Sun
Keyword(s):  
Social Networks ◽  
Social Media ◽  
Health Communication ◽  
Online Social Networks ◽  
Negative Association ◽  
Study Analysis ◽  
Positive Role ◽  
Political Issues ◽  
Social Media Platforms ◽  
Cyber Space

COVID-19 pandemic is a global Crisis, social media platforms have been a significant site of getting information and arouse discussions. However, social bots have risen on the online social networks, social bots are applications that existing in cyber space merely and they can mimic human users to interact with you following their own logic, there are the features of “Intangible”、“personate” and “automatic”. Evidence suggests that social bots did harm to the Health Communication during COVID-19 pandemic, researchers found that social bots contributed to diffuse political issues stir negative emotions, spread rumor. Social bots often have a negative association, but there are many bots which perform benign tasks. This study analysis the reasons bots performed badly in COVID-19 pandemic first, then discuss about how to turn the “threats” to “treatments”, proving that social bots can act as a positive role in different periods of Health Emergencies.

scholarly journals Healthy Food on the Twitter Social Network: Vegan, Homemade, and Organic Food

2021 ◽  
Vol 18 (7) ◽  
pp. 3815
Author(s):  
Ladislav Pilař ◽  
Lucie Kvasničková Stanislavská ◽  
Roman Kvasnička
Keyword(s):  
Social Networks ◽  
Social Media ◽  
Social Network ◽  
Online Social Networks ◽  
Social Networking Sites ◽  
Healthy Lifestyle ◽  
Healthy Food ◽  
Communication Analysis ◽  
Research Areas ◽  
Social Media Platforms

Online social networks have become an everyday aspect of many people’s lives. Users spend more and more time on these platforms and, through their interactions on social media platforms, they create active and passive digital footprints. These data have a strong potential in many research areas; indeed, understanding people’s communication on social media is essential for understanding their attitudes, experiences, behaviors and values. Researchers have found that the use of social networking sites impacts eating behavior; thus, analyzing social network data is important for understanding the meaning behind expressions used in the context of healthy food. This study performed a communication analysis of data from the social network Twitter, which included 666,178 messages posted by 168,134 individual users. These data comprised all tweets that used the #healthyfood hashtag between 2019 and 2020 on Twitter. The results revealed that users most commonly associate healthy food with a healthy lifestyle, diet, and fitness. Foods associated with this hashtag were vegan, homemade, and organic. Given that people change their behavior according to other people’s behavior on social networks, these data could be used to identify current and future associations with current and future perceptions of healthy food characteristics.

scholarly journals A Multichannel Deep Learning Framework for Cyberbullying Detection on Social Media

2021 ◽  
Author(s):  
Munif Alotaibi ◽  
Bandar Alotaibi ◽  
Abdul Razaque
Keyword(s):  
Social Networks ◽  
Social Media ◽  
Deep Learning ◽  
Language Processing ◽  
Online Social Networks ◽  
Hate Speech ◽  
Message Service ◽  
Integral Role ◽  
Social Media Platforms ◽  
Cyberbullying Detection

Online social networks (OSNs) play an integral role in facilitating social interaction; however, these social networks increase antisocial behavior, such as cyberbullying, hate speech, and trolling. Aggression or hate speech that takes place through short message service (SMS) or the Internet (e.g., in social media platforms) is known as cyberbullying. Therefore, automatic detection utilizing natural language processing (NLP) is a necessary first step that helps prevent cyberbullying. This research proposes an automatic cyberbullying method to detect aggressive behavior using a consolidated deep learning model. This technique utilizes multichannel deep learning based on three models, namely, the bidirectional gated recurrent unit (BiGRU), transformer block, and convolutional neural network (CNN), to classify Twitter comments into two categories: aggressive and not aggressive. Three well-known hate speech datasets were combined to evaluate the performance of the proposed method. The proposed method achieved promising results. The accuracy of the proposed method was approximately 88%.

scholarly journals A Multichannel Deep Learning Framework for Cyberbullying Detection on Social Media

Electronics ◽  
2021 ◽  
Vol 10 (21) ◽  
pp. 2664
Author(s):  
Munif Alotaibi ◽  
Bandar Alotaibi ◽  
Abdul Razaque
Keyword(s):  
Social Networks ◽  
Social Media ◽  
Deep Learning ◽  
Language Processing ◽  
Online Social Networks ◽  
Hate Speech ◽  
Message Service ◽  
Integral Role ◽  
Social Media Platforms ◽  
Cyberbullying Detection

Online social networks (OSNs) play an integral role in facilitating social interaction; however, these social networks increase antisocial behavior, such as cyberbullying, hate speech, and trolling. Aggression or hate speech that takes place through short message service (SMS) or the Internet (e.g., in social media platforms) is known as cyberbullying. Therefore, automatic detection utilizing natural language processing (NLP) is a necessary first step that helps prevent cyberbullying. This research proposes an automatic cyberbullying method to detect aggressive behavior using a consolidated deep learning model. This technique utilizes multichannel deep learning based on three models, namely, the bidirectional gated recurrent unit (BiGRU), transformer block, and convolutional neural network (CNN), to classify Twitter comments into two categories: aggressive and not aggressive. Three well-known hate speech datasets were combined to evaluate the performance of the proposed method. The proposed method achieved promising results. The accuracy of the proposed method was approximately 88%.

Is Social Media Marketing Really Working?

2013 ◽  
pp. 174-193 ◽  
Author(s):  
Felipe Uribe Saavedra ◽  
Josep Rialp Criado ◽  
Joan Llonch Andreu
Keyword(s):  
Social Networks ◽  
Social Media ◽  
Online Social Networks ◽  
Business Performance ◽  
Dynamic Capabilities ◽  
Social Media Marketing ◽  
Marketing Tool ◽  
Use Of Social Media ◽  
Marketing Intensity ◽  
The Impact

Online social networks have become the fastest growing phenomenon on the Internet and firms are beginning to take advantage of them as a marketing tool. However, the strategic importance of social media marketing is not yet clear, given the novelty and the difficulty of measuring its impact on business performance. This study uses data from 191 Spanish firms from several sectors to measure the impact of the intensity of use of social media marketing on the relationship between the dynamic capabilities of market orientation and entrepreneurial orientation, and business performance. The results provide evidence of the moderating effects of social media marketing intensity on the strength of the mentioned relations and the importance of a strong and committed marketing strategy on digital social networks for businesses.

Social Media Analytics

2013 ◽  
pp. 212-228
Author(s):  
Sunil Kr Pandey ◽  
Vineet Kansal
Keyword(s):  
Social Networks ◽  
Social Media ◽  
Online Social Networks ◽  
Medical Science ◽  
Multimedia Data ◽  
Social Media Analytics ◽  
Multimedia Networks ◽  
Linkage Data ◽  
Insurance Sector ◽  
Tremendous Amount

Many popular online social networks such as Twitter, LinkedIn, and Facebook have become increasingly popular. In addition, a number of multimedia networks such as Flickr have also seen an increasing level of popularity in recent years. Many such social networks are extremely rich in content, and contain tremendous amount of content and linkage data which can be leveraged for analysis. The linkage data is essentially the graph structure of the social network and the communications between entities; whereas the content data contains the text, images and other multimedia data in the network. The growth of the usage and penetration of social media in the recent years has been enormous and unprecedented. This significant increase in its usage and increased number of users, there has been trend of a substantial increase in the volume of information generated by users of social media. Irrespective of primary domain in which organization is operating in to, whether it is insurance sector, social media (including facebook, twitter etc), medical science, banking etc. Virtually a large number of varying nature and services of organizations are making significant investments in social media. But it is also true that many are not systematically analyzing the valuable information that is resulting from their investments. This chapter aims at providing a data-centric view of online social networks; a topic which has been missing from much of the literature and to draw unanswered research issues which can be further explored to strengthen this area.

Is Social Media Marketing Really Working?

2014 ◽  
pp. 1260-1279 ◽  
Author(s):  
Felipe Uribe Saavedra ◽  
Josep Rialp Criado ◽  
Joan Llonch Andreu
Keyword(s):  
Social Networks ◽  
Social Media ◽  
Online Social Networks ◽  
Business Performance ◽  
Dynamic Capabilities ◽  
Social Media Marketing ◽  
Marketing Tool ◽  
Use Of Social Media ◽  
Marketing Intensity ◽  
The Impact

Online social networks have become the fastest growing phenomenon on the Internet and firms are beginning to take advantage of them as a marketing tool. However, the strategic importance of social media marketing is not yet clear, given the novelty and the difficulty of measuring its impact on business performance. This study uses data from 191 Spanish firms from several sectors to measure the impact of the intensity of use of social media marketing on the relationship between the dynamic capabilities of market orientation and entrepreneurial orientation, and business performance. The results provide evidence of the moderating effects of social media marketing intensity on the strength of the mentioned relations and the importance of a strong and committed marketing strategy on digital social networks for businesses.

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