scholarly journals Steganographic visual story with mutual-perceived joint attention

2021 ◽  
Vol 2021 (1) ◽  
Author(s):  
Yanyang Guo ◽  
Hanzhou Wu ◽  
Xinpeng Zhang
Keyword(s):  
Social Support ◽  
Social Media ◽  
Deep Learning ◽  
Joint Attention ◽  
Online Social Networks ◽  
Generation Model ◽  
High Quality ◽  
Visual Storytelling ◽  
The Social ◽  
User Intervention

AbstractSocial media plays an increasingly important role in providing information and social support to users. Due to the easy dissemination of content, as well as difficulty to track on the social network, we are motivated to study the way of concealing sensitive messages in this channel with high confidentiality. In this paper, we design a steganographic visual stories generation model that enables users to automatically post stego status on social media without any direct user intervention and use the mutual-perceived joint attention (MPJA) to maintain the imperceptibility of stego text. We demonstrate our approach on the visual storytelling (VIST) dataset and show that it yields high-quality steganographic texts. Since the proposed work realizes steganography by auto-generating visual story using deep learning, it enables us to move steganography to the real-world online social networks with intelligent steganographic bots.

Empathy not quarantined: Association between people’s empathy to others and the COVID-19 pandemic situations based on a national survey study (Preprint)

2020 ◽  
Author(s):  
Zhibin Jiang ◽  
Fan Yang ◽  
Bu Zhong ◽  
Xuebing Qin
Keyword(s):  
Social Support ◽  
Social Media ◽  
National Survey ◽  
Low Income ◽  
Rural Areas ◽  
Critical Role ◽  
Affective Response ◽  
Survey Study ◽  
Income Levels ◽  
The Social

BACKGROUND The Covid-19 pandemic had turned the world upside down, but not much is known about how people’s empathy might be affected by the pandemic. OBJECTIVE This study examined 1) how empathy towards others might be influenced by the social support people obtained by using social media; and 2) how the individual demographics (e.g., age, income) may affect empathy. METHODS A national survey (N = 943) was conducted in China in February 2020, in which the participants read three real scenarios about low-income urban workers (Scenario I), small business owners in cities (Scenario II), and farmers in rural areas (Scenario III) who underwent hardship due to COVID-19. After exposure to others’ difficulties in the scenarios, the participants’ empathy and anxiety levels were measured. We also measured the social support they had by using social media. RESULTS Results show that social support not only positively impacted empathy, β = .30, P < .001 for Scenario I, β = .30, P < .001 for Scenario II, and β = .29, P < .001 for Scenario III, but also interacted with anxiety in influencing the degree to which participants could maintain empathy towards others, β = .08, P = .010 for Scenario I, and β = .07, P = .033 for scenario II. Age negatively predicted empathy for Scenario I, β = -.08, P = .018 and Scenario III, β = -.08, P = .009, but not for Scenario II, β = -.03, P = .40. Income levels – low, medium, high – positively predicted empathy for Scenario III, F (2, 940) = 8.10, P < .001, but not for Scenario I, F (2, 940) = 2.14, P = .12, or Scenario II, F (2, 940) = 2.93, P = .06. Participants living in big cities expressed greater empathy towards others for Scenario III, F (2, 940) = 4.03, P =.018, but not for Scenario I, F (2, 940) = .81, P = .45, or Scenario II, F (2, 940) = 1.46, P =.23. CONCLUSIONS This study contributes to the literature by discovering the critical role empathy plays in people’s affective response to others during the pandemic. Anxiety did not decrease empathy. However, those gaining more social support on social media showed more empathy for others. Those who resided in cities with higher income levels were more empathetic during the COVID-19 outbreak. This study reveals that the social support people obtained helped maintain empathy to others, making them resilient in challenging times.

Towards a Unified Semantic Model for Online Social Networks to Ensure Interoperability and Aggregation for Analysis

2018 ◽  
pp. 978-1003
Author(s):  
Asmae El Kassiri ◽  
Fatima-Zahra Belouadha
Keyword(s):  
Social Networks ◽  
Social Media ◽  
Online Social Networks ◽  
Semantic Model ◽  
Semantic Web Technologies ◽  
Web Technologies ◽  
Knowing That ◽  
The Social ◽  
Industrial Projects ◽  
Media Organizations

The Online Social Networks (OSN) have a positive evolution due to the diversity of social media and the increase in the number of users. The revenue of the social media organizations is generated from the analysis of users' profiles and behaviors, knowing that surfers maintain several accounts on different OSNs. To satisfy its users, the social media organizations have initiated projects for ensuring interoperability to allow for users creating other accounts on other OSN using an initial account, and sharing content from one media to others. Believing that the future generations of Internet will be based on the semantic web technologies, multiple academic and industrial projects have emerged with the objective of modeling semantically the OSNs to ensure interoperability or data aggregation and analysis. In this chapter, we present related works and argue the necessity of a unified semantic model (USM) for OSNs; we introduce a kernel of a USM using standard social ontologies to support the principal social media and it can be extended to support other future social media.

The Alt-social Network of Natalie Bookchin’s Testament

2016 ◽  
Vol 18 (5) ◽  
pp. 459-477
Author(s):  
Sarah Whitcomb Laiola
Keyword(s):  
Data Mining ◽  
Social Networks ◽  
Social Media ◽  
Social Network ◽  
Online Social Networks ◽  
Social Web ◽  
User Interactions ◽  
Media Criticism ◽  
Direct Translation ◽  
The Social

This article addresses issues of user precarity and vulnerability in online social networks. As social media criticism by Jose van Dijck, Felix Stalder, and Geert Lovink describes, the social web is a predatory system that exploits users’ desires for connection. Although accurate, this critical description casts the social web as a zone where users are always already disempowered, so fails to imagine possibilities for users beyond this paradigm. This article examines Natalie Bookchin’s composite video series, Testament, as it mobilizes an alt-(ernative) social network of vernacular video on YouTube. In the first place, the alt-social network works as an iteration of “tactical media” to critically reimagine empowered user-to-user interactions on the social web. In the second place, it obfuscates YouTube’s data-mining functionality, so allows users to socialize online in a way that evades their direct translation into data and the exploitation of their social labor.

scholarly journals Emotion shapes the diffusion of moralized content in social networks

2017 ◽  
Vol 114 (28) ◽  
pp. 7313-7318 ◽  
Author(s):  
William J. Brady ◽  
Julian A. Wills ◽  
John T. Jost ◽  
Joshua A. Tucker ◽  
Jay J. Van Bavel
Keyword(s):  
Social Networks ◽  
Social Media ◽  
Online Social Networks ◽  
Political Debate ◽  
Political Issues ◽  
Emotional Words ◽  
Social Media Networks ◽  
Political Ideas ◽  
The Social ◽  
Network Methods

Political debate concerning moralized issues is increasingly common in online social networks. However, moral psychology has yet to incorporate the study of social networks to investigate processes by which some moral ideas spread more rapidly or broadly than others. Here, we show that the expression of moral emotion is key for the spread of moral and political ideas in online social networks, a process we call “moral contagion.” Using a large sample of social media communications about three polarizing moral/political issues (n = 563,312), we observed that the presence of moral-emotional words in messages increased their diffusion by a factor of 20% for each additional word. Furthermore, we found that moral contagion was bounded by group membership; moral-emotional language increased diffusion more strongly within liberal and conservative networks, and less between them. Our results highlight the importance of emotion in the social transmission of moral ideas and also demonstrate the utility of social network methods for studying morality. These findings offer insights into how people are exposed to moral and political ideas through social networks, thus expanding models of social influence and group polarization as people become increasingly immersed in social media networks.

scholarly journals Machine Learning Approach for User Accounts Identification with Unwanted Information and data

2018 ◽  
Vol 2 (3) ◽  
pp. 119-127 ◽  
Author(s):  
Abhishek Kumar ◽  
TVM SAIRAM
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Online Social Networks ◽  
Social Media Analytics ◽  
Learning Approach ◽  
Efficient Manner ◽  
Implementation Phase ◽  
Large Numbers ◽  
The Social ◽  
Machine Learning Approach

Machine Learning used for many real-time issues in many organizations and the purpose of social media analytics machine learning models are used most prominently and to identify the genuine accounts and the information in the social media we are here with a new pattern of identification. In this pattern of the model, we are proposing some words which are hidden to identify the accounts with fake data and the some of the steps we are proposing will help to identify the fake and unwanted accounts in Facebook in an efficient manner. Clustering in machine learning will be used, and before that, we are proposing a suitable architecture and the process flow which can identify the fake and suspicious accounts in the social media. This article will be on machine learning implementations and will be working on OSN (online social networks). Our work will be more on Facebook which is maintaining more amount of accounts and identifying which are overruling the rules on privacy and protection of the user content. Machine learning supervised models will be used for text classification, and CNN of unsupervised learning performs the image classification, and the explanation will be given in the implementation phase. There are large numbers of algorithms we can consider for machine learning implementations in the social networking and here we considered mainly on CNN because of having the feasibility of implementation in different rules and we can eliminate the features we don’t need. Feature extraction is quite simple using CNN.

scholarly journals The Effect of Social Media Use on Depressive Symptoms in Older Adults with Self-Reported Hearing Impairment: An Empirical Study

Healthcare ◽  
2021 ◽  
Vol 9 (11) ◽  
pp. 1403
Author(s):  
Yiming Ma ◽  
Changyong Liang ◽  
Xuejie Yang ◽  
Haitao Zhang ◽  
Shuping Zhao ◽  
...  
Keyword(s):  
Mental Health ◽  
Social Support ◽  
Social Media ◽  
Depressive Symptoms ◽  
Older People ◽  
Hearing Impairment ◽  
Social Isolation ◽  
Social Relations ◽  
The Social ◽  
Subjective Aging

Older people with hearing impairment are more likely to develop depressive symptoms due to physical disability and loss of social communication. This study investigated the effects of social media on social relations, subjective aging, and depressive symptoms in these older adults based on the stimulus-organism-response (S-O-R) framework. It provides new empirical evidence to support improving the mental health and rebuilding the social relations of older people. A formal questionnaire was designed using the Wenjuanxing platform and distributed online through WeChat; 643 valid questionnaires were received from older people with self-reported hearing impairments, and SmartPLS 3.28 was used to analyze the data. The results show that (1) social media significantly impacts the social relations of older people with hearing impairment (social networks, β = 0.132, T = 3.444; social support, β = 0.129, T = 2.95; social isolation, β = 0.107, T = 2.505). (2) For these older people, social isolation has the biggest impact on their psychosocial loss (β = 0.456, T = 10.458), followed by the impact of social support (β = 0.103, T = 2.014); a hypothesis about social network size was not confirmed (β = 0.007, T = 0.182). Both social media (β = 0.096, T = 2.249) and social support (β = 0.174, T = 4.434) significantly affect the self-efficacy of hearing-impaired older people. (3) Both subjective aging (psychosocial loss, β = 0.260, T = 6.036; self-efficacy, β = 0.106, T = 3.15) and social isolation (β = 0.268, T = 6.307) significantly affect depressive symptoms in older people with hearing impairment. This study expands the theories of social media aging cognition, social support, and social networks and can provide practical contributions to the social media use and mental health of special persons 60 years and older.

scholarly journals Sentiment Analysis Of Twitter Data By Using Deep Learning And Machine Learning

2021 ◽  
Vol 12 (6) ◽  
pp. 962-970
Author(s):  
Prof. Manisha Sachin Dabade, Et. al.
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Deep Learning ◽  
Sentiment Analysis ◽  
Daily Monitoring ◽  
Fine Grain ◽  
Large Size ◽  
Twitter Data ◽  
The Social ◽  
Enormous Amount

In today’s world, social media is viral and easily accessible. The Social media sites like Twitter, Facebook, Tumblr, etc. are a primary and valuable source of information.Twitter is a micro-blogging platform, and it provides an enormous amount of data. Such type of information can use for different sentiment analysis applications such as reviews, predictions, elections, marketing, etc. It is one of the most popular sites where peoples write tweets, retweets, and interact daily. Monitoring and analyzing these tweets give valuable feedback to users. Due to this data's large size, sentiment analysis is using to analyze this data without going through millions of tweets manually. Any user writes their reviews about different products, topics, or events on Twitter, called tweets and retweets. People also use emojis such as happy, sad, and neutral in expressing their emotions, so these sites contain expansive volumes of unprocessed data called raw data. The main goal of this research is to recognize the algorithms by using Machine Learning Classifiers. The study intends to categorize Fine-grain sentiments within Tweets of Vaccination (89974 tweets) through machine learning and a deep learning approach. The study takes consideration of both labeled and unlabeled data. It also detects emojis from tweets using machine learning libraries like Textblob, Vadar, Fast text, Flair, Genism, spaCy, and NLTK.

scholarly journals Motivations for Virtual Community Participation in Social Commerce: Customers and Sellers in the Malay Lifestyle Blogging Community

2021 ◽  
Author(s):  
◽  
Syahida Hassan
Keyword(s):  
Social Support ◽  
Social Media ◽  
Large Scale ◽  
Virtual Communities ◽  
Virtual Community ◽  
Structured Interview ◽  
Ease Of Use ◽  
Social Commerce ◽  
Commercial Activities ◽  
The Social

<p>Although the field of social commerce has gained a lot of attention recently, there are many areas that still remain unexplored. A new phenomenon emerging within virtual communities is a blurring between social and commercial activities. To date, scholars in the social commerce literature have either focused on customers in the community or on medium to large scale businesses. There has been little research on social commerce communities which include micro-businesses despite their rapid growth in South East Asian countries.  This study explores a social commerce community of Malay lifestyle bloggers, who are a subset of the Malaysian blogosphere community. Bloggers begin by using the personal genre, some then move on to set up online businesses using their personal blogs as a platform. The characteristic of blogging’s ease of use means there are low barriers to starting a small business, merging blogging and commerce. This changes the nature of the community by bringing in a new relationship, as well as relationships between bloggers and readers, there are now also relationships between sellers and customers.  This study aims to understand the motivations for both sellers and customers, and how their relationships as bloggers and readers influence their participation in social commerce within the same community. To address the research objective, 20 sellers and 21 customers who also play a role as bloggers or readers were interviewed. In-depth interviews using laddering and semi-structured interview techniques were carried out to explore social commerce behaviour, the perceived consequences, and goals or values of participation. In addition, observation was also conducted on the platform used by the sellers. Data was coded using NVivo whilst the themes arising from the coding process were transformed into an implication matrix and hierarchical value map using Ladderux software.  This study found that strong ties within the community, influenced by homophily and the sense of virtual community, motivated the customers to participate in commercial activities in order to obtain their goals which included a sense of obligation, loyalty, satisfaction and self-esteem. The relationships influenced customers to trust each other, provide social support and made purchasing products more convenient. Sellers were influenced by the convenience of using social media and the social support provided by the customers which helped them to achieve their goals which are profit and business sustainability.  This study contributes to social commerce theory by highlighting an underexplored type of social commerce setting and addressing how trust can be transferred from social to commercial activities. The findings provide a useful insight for businesses, regardless of their size, to build an understanding of the need to create a good relationship with their customers. For macro-businesses, this model can be used to identify what is lacking in their social media marketing strategy.</p>

scholarly journals THE ROLE OF SOCIAL COMMERCE CONSTRUCTS, SOCIAL SUPPORT, AND TRUST IN COMMUNITY ON SOCIAL COMMERCE ACTIVITIES

2018 ◽  
Vol 1 (2) ◽  
pp. 74-83
Author(s):  
Amir Hidayatulloh
Keyword(s):  
Social Support ◽  
Social Media ◽  
Emotional Support ◽  
Sampling Technique ◽  
Information Support ◽  
Social Commerce ◽  
The Social ◽  
Convenience Sampling ◽  
The Individual

This study  aims  to  analyze  social  commerce  constructs, social  support,  and  individual  trust in the  community   in   social   commerce   activities.   Social   support   includes   emotional   support  and informational  support.  The population  was  social  media  users, while  the  samples were  social media users who had made purchase at least two transactions through social media. The sampling technique was convenience sampling. Totally, 162 respondents were involved. Hypothesis testing was  done using  Warp PLS. This study  reveals that individual  trust in  the community  can be built directly  through  the social  commerce  constructs. These  constructs affects both  emotional  support and information support, in which they will ultimately affect the individual trust in the community. Furthermore,  social  commerce  intention  is influenced  by  individual  trust in  the community  and emotional  support.  However,  information  support does not  affect  the social commerce  intention.

scholarly journals Oversharenting and family life

2020 ◽  
Vol 8 (11) ◽  
pp. 155-170
Author(s):  
Renata Soares Martins ◽  
Suely Aparecida do Nascimento Mascarenhas ◽  
Gisele Cristina Resende
Keyword(s):  
Social Networks ◽  
Social Media ◽  
Interpersonal Relationships ◽  
Family Life ◽  
Online Social Networks ◽  
The Internet ◽  
Communications Technology ◽  
The Social ◽  
The Family ◽  
High Degree

This article invites us to reflect on oversharenting and family life that, owing to the proliferation of communications technology and the internet, is intersected by digital cyberculture. The research was carried out on the social network, using the method of searching by hashtag. The results showed that during 2018 in two weeks, 20,781 posts were made using the hashtag “minidiva” and 1,679 with the hashtag “miniblogger”, from which three posts were collected each day. Netnography was used to analyze the images and categorize them: (1) oversharenting and family life, (2) social media and child consumption, (3) child adultization. It was concluded that online social networks (Instagram) are spaces where interpersonal relationships; it was seen that the act of consuming gained relevance in the family and that the child’s exposure occurs without awareness, which can cause a high degree of exposure and consequently have adverse effects for everyone.

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