Social Media Content Analysis

2021 ◽  
pp. 156-174
Author(s):  
D. Sudaroli Vijayakumar ◽  
Senbagavalli M. ◽  
Jesudas Thangaraju ◽  
Sathiyamoorthi V.
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Media Content ◽  
Media Content Analysis ◽  
Comprehensive Overview ◽  
Case Examples ◽  
The Social ◽  
The Right ◽  
Audio Video ◽  
Suitable Case

Today's wealth and value are data. Data, used sensibly, are making wonders to make wise decisions for individuals, corporates, etc. The era of spending time with an individual to understand them better is gone. Individual's interests, requirements are identified easily by observing the activities an individual performs in social media. Social media, started as a tool for interaction, has grown as a platform to make and promote business. Social media content is unavoidable as the data that are going to be dealt with is huge in volume, variety, and velocity. The demand for using machine learning in analysing social media content is increasing at a faster pace in identifying influencers, demands of individuals. However, the real complexity lies in making the data from social media suitable for analysis. The type of data from social media content may be audio, video, image. The chapter attempts to give a comprehensive overview of the various pre-processing methods involved in dealing the social media content and the usage of right algorithms at the right time with suitable case examples.

scholarly journals Analyzing Social Media to Explore the Attitudes and Behaviors Following the Announcement of Successful COVID-19 Vaccine Trials: Infodemiology Study

10.2196/28800 ◽  
2021 ◽  
Vol 1 (1) ◽  
pp. e28800
Author(s):  
Jean-Christophe Boucher ◽  
Kirsten Cornelson ◽  
Jamie L Benham ◽  
Madison M Fullerton ◽  
Theresa Tang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Content Analysis ◽  
Social Network ◽  
Multinational Corporations ◽  
Vaccine Hesitancy ◽  
Media Content ◽  
Media Content Analysis ◽  
Vaccine Trials ◽  
The Government

Background The rollout of COVID-19 vaccines has brought vaccine hesitancy to the forefront in managing this pandemic. COVID-19 vaccine hesitancy is fundamentally different from that of other vaccines due to the new technologies being used, rapid development, and widespread global distribution. Attitudes on vaccines are largely driven by online information, particularly information on social media. The first step toward influencing attitudes about immunization is understanding the current patterns of communication that characterize the immunization debate on social media platforms. Objective We aimed to evaluate societal attitudes, communication trends, and barriers to COVID-19 vaccine uptake through social media content analysis to inform communication strategies promoting vaccine acceptance. Methods Social network analysis (SNA) and unsupervised machine learning were used to characterize COVID-19 vaccine content on Twitter globally. Tweets published in English and French were collected through the Twitter application programming interface between November 19 and 26, 2020, just following the announcement of initial COVID-19 vaccine trials. SNA was used to identify social media clusters expressing mistrustful opinions on COVID-19 vaccination. Based on the SNA results, an unsupervised machine learning approach to natural language processing using a sentence-level algorithm transfer function to detect semantic textual similarity was performed in order to identify the main themes of vaccine hesitancy. Results The tweets (n=636,516) identified that the main themes driving the vaccine hesitancy conversation were concerns of safety, efficacy, and freedom, and mistrust in institutions (either the government or multinational corporations). A main theme was the safety and efficacy of mRNA technology and side effects. The conversation around efficacy was that vaccines were unlikely to completely rid the population of COVID-19, polymerase chain reaction testing is flawed, and there is no indication of long-term T-cell immunity for COVID-19. Nearly one-third (45,628/146,191, 31.2%) of the conversations on COVID-19 vaccine hesitancy clusters expressed concerns for freedom or mistrust of institutions (either the government or multinational corporations) and nearly a quarter (34,756/146,191, 23.8%) expressed criticism toward the government’s handling of the pandemic. Conclusions Social media content analysis combined with social network analysis provides insights into the themes of the vaccination conversation on Twitter. The themes of safety, efficacy, and trust in institutions will need to be considered, as targeted outreach programs and intervention strategies are deployed on Twitter to improve the uptake of COVID-19 vaccination.

scholarly journals Analyzing Social Media to Explore the Attitudes and Behaviors Following the Announcement of Successful COVID-19 Vaccine Trials: Infodemiology Study (Preprint)

2021 ◽  
Author(s):  
Jean-Christophe Boucher ◽  
Kirsten Cornelson ◽  
Jamie L Benham ◽  
Madison M Fullerton ◽  
Theresa Tang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Content Analysis ◽  
Social Network ◽  
Multinational Corporations ◽  
Vaccine Hesitancy ◽  
Media Content ◽  
Media Content Analysis ◽  
Vaccine Trials ◽  
The Government

BACKGROUND The rollout of COVID-19 vaccines has brought vaccine hesitancy to the forefront in managing this pandemic. COVID-19 vaccine hesitancy is fundamentally different from that of other vaccines due to the new technologies being used, rapid development, and widespread global distribution. Attitudes on vaccines are largely driven by online information, particularly information on social media. The first step toward influencing attitudes about immunization is understanding the current patterns of communication that characterize the immunization debate on social media platforms. OBJECTIVE We aimed to evaluate societal attitudes, communication trends, and barriers to COVID-19 vaccine uptake through social media content analysis to inform communication strategies promoting vaccine acceptance. METHODS Social network analysis (SNA) and unsupervised machine learning were used to characterize COVID-19 vaccine content on Twitter globally. Tweets published in English and French were collected through the Twitter application programming interface between November 19 and 26, 2020, just following the announcement of initial COVID-19 vaccine trials. SNA was used to identify social media clusters expressing mistrustful opinions on COVID-19 vaccination. Based on the SNA results, an unsupervised machine learning approach to natural language processing using a sentence-level algorithm transfer function to detect semantic textual similarity was performed in order to identify the main themes of vaccine hesitancy. RESULTS The tweets (n=636,516) identified that the main themes driving the vaccine hesitancy conversation were concerns of safety, efficacy, and freedom, and mistrust in institutions (either the government or multinational corporations). A main theme was the safety and efficacy of mRNA technology and side effects. The conversation around efficacy was that vaccines were unlikely to completely rid the population of COVID-19, polymerase chain reaction testing is flawed, and there is no indication of long-term T-cell immunity for COVID-19. Nearly one-third (45,628/146,191, 31.2%) of the conversations on COVID-19 vaccine hesitancy clusters expressed concerns for freedom or mistrust of institutions (either the government or multinational corporations) and nearly a quarter (34,756/146,191, 23.8%) expressed criticism toward the government’s handling of the pandemic. CONCLUSIONS Social media content analysis combined with social network analysis provides insights into the themes of the vaccination conversation on Twitter. The themes of safety, efficacy, and trust in institutions will need to be considered, as targeted outreach programs and intervention strategies are deployed on Twitter to improve the uptake of COVID-19 vaccination.

ISIS Propaganda

2019 ◽  
Keyword(s):  
Social Sciences ◽  
Social Media ◽  
Political Violence ◽  
International Security ◽  
Comprehensive Overview ◽  
Full Spectrum ◽  
Theoretical Perspectives ◽  
The Social ◽  
One Stop ◽  
Stop Point

ISIS Propaganda offers a comprehensive overview and analysis of the Islamic State’s (IS) propaganda. Combining a range of different theoretical perspectives from across the social sciences and using rigorous methods, the authors pursue several interconnected tasks. They trace the origins of IS’s message, they lay bare the strategic logic guiding its evolution, they examine each of its many components (magazines, videos, music, social media, etc.) and show how they work together to radicalize audiences’ worldviews, and they highlight the challenges such a “full-spectrum propaganda” raises in terms of counterterrorism. The volume hence not only represents a one-stop point for any analyst of IS and Salafi-jihadism, but also a rich contribution to the study of text and visual propaganda, radicalization and political violence, and international security.

scholarly journals Examining Perceived and Projected Destination Image: A Social Media Content Analysis

2021 ◽  
Vol 13 (6) ◽  
pp. 3354
Author(s):  
Wei Sun ◽  
Shoulian Tang ◽  
Fang Liu
Keyword(s):  
Social Media ◽  
Content Analysis ◽  
Destination Image ◽  
Media Content ◽  
Media Content Analysis ◽  
Poor Understanding ◽  
Practical Implications

Destination image has been extensively studied in tourism and marketing, but the questions surrounding the discrepancy between the projected (perceptions from the National Tourism Organizations) and perceived destination image (perceptions from tourists) as well as how the discrepancy may influence sustainable experience remain unclear. Poor understanding of the discrepancy may cause tourist confusion and misuse of resources. The aim of this study is to empirically investigate if the perceived (by tourists) and projected (by NTOs) destination image are significantly different in both cognitive and affective aspects. Through a comprehensive social media content analysis of the NTO-generated and tourist-generated-contents (TGC), the current study identifies numerous gaps between the projected and perceived destination image, which offers some important theoretical and practical implications on destination management and marketing.

scholarly journals Linguistic drivers of misinformation diffusion on social media during the COVID-19 pandemic

2021 ◽  
Author(s):  
Giandomenico Di Domenico ◽  
Annamaria Tuan ◽  
Marco Visentin
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
New Technologies ◽  
Fake News ◽  
Conspiracy Theories ◽  
Social Media Platform ◽  
The Social ◽  
Media Platform ◽  
Textual Cues ◽  
Twitter Users

AbstractIn the wake of the COVID-19 pandemic, unprecedent amounts of fake news and hoax spread on social media. In particular, conspiracy theories argued on the effect of specific new technologies like 5G and misinformation tarnished the reputation of brands like Huawei. Language plays a crucial role in understanding the motivational determinants of social media users in sharing misinformation, as people extract meaning from information based on their discursive resources and their skillset. In this paper, we analyze textual and non-textual cues from a panel of 4923 tweets containing the hashtags #5G and #Huawei during the first week of May 2020, when several countries were still adopting lockdown measures, to determine whether or not a tweet is retweeted and, if so, how much it is retweeted. Overall, through traditional logistic regression and machine learning, we found different effects of the textual and non-textual cues on the retweeting of a tweet and on its ability to accumulate retweets. In particular, the presence of misinformation plays an interesting role in spreading the tweet on the network. More importantly, the relative influence of the cues suggests that Twitter users actually read a tweet but not necessarily they understand or critically evaluate it before deciding to share it on the social media platform.

Marijuana and Glaucoma: A Social Media Content Analysis

2020 ◽  
Author(s):  
Jing (Sasha) Jia ◽  
Nikki Mehran ◽  
Robert Purgert ◽  
Qiang (Ed) Zhang ◽  
Daniel Lee ◽  
...  
Keyword(s):  
Social Media ◽  
Content Analysis ◽  
Media Content ◽  
Media Content Analysis

scholarly journals ‘Wife, Mommy, Pastor and Friend’: The Rise of Female Evangelical Microcelebrities

Religions ◽  
2021 ◽  
Vol 12 (9) ◽  
pp. 758
Author(s):  
Katie Christine Gaddini
Keyword(s):  
Social Media ◽  
Digital Media ◽  
Media Analysis ◽  
Media Content ◽  
Religious Authority ◽  
Social Media Analysis ◽  
The Social ◽  
Prosperity Theology ◽  
Evangelical Women ◽  
Ethnographic Data

The popularity of digital media has spurred what has been called a “crisis of authority”. How do female evangelical microcelebrities figure in this crisis? Many of these women belong to churches led by male pastors, have amassed a large following online, and are sought-after speakers and teachers. This paper analyses how gender, religious authority, and the digital sphere collide through the rise of female evangelical microcelebrities. Bringing together ethnographic data, textual analysis, and social media analysis of six prominent women, I emphasize the power of representation to impact religious practices and religious meaning. This article examines how evangelical women are performing and negotiating their legitimacy as the Internet and fluid geographical boundaries challenge local models of religious authority. Moving away from a binary perspective of “having” or “not having” authority, this paper considers the various spheres of authority that evangelical microcelebrities occupy, including normative womanhood, prosperity theology, and politics. Finally, by examining the social media content put forth by female evangelical microcelebrities, I interrogate the political stakes of evangelical women’s authority.

Sign in / Sign up

Export Citation Format

Share Document