Exploring Public Perceptions of Native-Born American Emigration Abroad and Renunciation of American Citizenship through Social Media

2017 ◽  
pp. 338-382 ◽  
Author(s):  
Shalin Hai-Jew
Keyword(s):  
Social Media ◽  
Research Literature ◽  
Social Phenomena ◽  
Social Media Data ◽  
The Public ◽  
The Social ◽  
American Citizenship ◽  
Social Media Platforms ◽  
Work Done ◽  
Media Data

There has been little work done on American emigration abroad and even less done on the formal renunciation of American citizenship. This chapter provides an overview of both phenomena in the research literature and then provides some methods for using the extraction of social media data and their visualization as a way of tapping into the public mindsets about these social phenomena. The software tools used include the following: Network Overview, Discovery and Exploration for Excel (NodeXL Basic), NVivo, and Maltego Carbon; the social media platforms used include the following: Wikipedia, YouTube, Twitter, and Flickr.

Exploring Public Perceptions of Native-Born American Emigration Abroad and Renunciation of American Citizenship through Social Media

2019 ◽  
pp. 60-101
Author(s):  
Shalin Hai-Jew
Keyword(s):  
Social Media ◽  
Research Literature ◽  
Social Phenomena ◽  
Social Media Data ◽  
The Public ◽  
The Social ◽  
American Citizenship ◽  
Social Media Platforms ◽  
Work Done ◽  
Media Data

There has been little work done on American emigration abroad and even less done on the formal renunciation of American citizenship. This chapter provides an overview of both phenomena in the research literature and then provides some methods for using the extraction of social media data and their visualization as a way of tapping into the public mindsets about these social phenomena. The software tools used include the following: Network Overview, Discovery and Exploration for Excel (NodeXL Basic), NVivo, and Maltego Carbon; the social media platforms used include the following: Wikipedia, YouTube, Twitter, and Flickr.

scholarly journals Review of Data Visualization for Social Media Postings

2018 ◽  
Vol 7 (4.38) ◽  
pp. 939
Author(s):  
Nur Atiqah Sia Abdullah ◽  
Hamizah Binti Anuar
Keyword(s):  
Social Media ◽  
Data Visualization ◽  
Line Graph ◽  
Data Types ◽  
Social Media Data ◽  
Data Analyst ◽  
The Social ◽  
Social Media Platforms ◽  
Types Of Information ◽  
Media Data

Facebook and Twitter are the most popular social media platforms among netizen. People are now more aggressive to express their opinions, perceptions, and emotions through social media platforms. These massive data provide great value for the data analyst to understand patterns and emotions related to a certain issue. Mining the data needs techniques and time, therefore data visualization becomes trending in representing these types of information. This paper aims to review data visualization studies that involved data from social media postings. Past literature used node-link diagram, node-link tree, directed graph, line graph, heatmap, and stream graph to represent the data collected from the social media platforms. An analysis by comparing the social media data types, representation, and data visualization techniques is carried out based on the previous studies. This paper critically discussed the comparison and provides a suggestion for the suitability of data visualization based on the type of social media data in hand.      

Social Media to Social Media Analytics

2019 ◽  
Vol 10 (2) ◽  
pp. 57-70 ◽  
Author(s):  
Vikas Kumar ◽  
Pooja Nanda
Keyword(s):  
Social Media ◽  
Social Media Analytics ◽  
Social Media Data ◽  
Ethical Concerns ◽  
Ethical Implications ◽  
The Social ◽  
Social Media Platforms ◽  
The Individual ◽  
The Impact ◽  
Media Data

With the amplification of social media platforms, the importance of social media analytics has exponentially increased for many brands and organizations across the world. Tracking and analyzing the social media data has been contributing as a success parameter for such organizations, however, the data is being poorly harnessed. Therefore, the ethical implications of social media analytics need to be identified and explored for both the organizations and targeted users of social media data. The present work is an exploratory study to identify the various techno-ethical concerns of social media engagement, as well as social media analytics. The impact of these concerns on the individuals, organizations, and society as a whole are discussed. Ethical engagement for the most common social media platforms has been outlined with a number of specific examples to understand the prominent techno-ethical concerns. Both the individual and organizational perspectives have been taken into account to identify the implications of social media analytics.

scholarly journals I Need Help, not Likes: Review of Models to Detect Depression through Social Media Data

2020 ◽  
Author(s):  
Ajay Agarwal
Keyword(s):  
Mental Health ◽  
Social Media ◽  
Language Processing ◽  
Review Paper ◽  
Significant Proportion ◽  
Social Media Data ◽  
Depression Detection ◽  
Social Media Platforms ◽  
Work Done ◽  
Media Data

Mental health is one of the most pressing challenges that we as humans are facing today. Despite the presence of various resources to detect and diagnose mental disorders, it continues to be an issue for a significant proportion of the world. Researchers are now exploring the application of natural language processing and deep learning on the diagnosis of these disorders.However, due to the lack of a systematic and organized set of data to train upon, many of them have often failed. In this review paper, we explore prominent techniques to diagnose a specific mental disorder – Depression. We analyze and review the work done in the field of depression detection from feeds of social media platforms. For our study, we investigated mainly 3 platforms – Facebook, Twitter, and Reddit.In the later sections of the paper, we aim to analyze another pressing topic of our study- The suitability of Social Media sources to act as data resources for the diagnostic tasks for mental health disorders. As the inclination of society towards social media increases, it evolves to become a better and robust outlet for people’s emotions and expressions. Whilst recording and obtaining logs of a particular patient’s visit with the therapist might be a more accurate description, however, it is one that is usually untimely and unlikely to be formally organized. In such scenarios, a patient’s posts on various social media platforms and his activity on the same provide a better turn of perspective to solve such problems.

scholarly journals “Taking the Temperature of the Room”

2020 ◽  
Vol 84 (S1) ◽  
pp. 236-256
Author(s):  
Shannon C McGregor
Keyword(s):  
Social Media ◽  
Public Opinion ◽  
Opinion Formation ◽  
Social Media Data ◽  
The Public ◽  
Media System ◽  
The Social ◽  
New Development ◽  
Use Of Social Media ◽  
Media Data

Abstract For most of the twentieth century, public opinion was nearly analogous with polling. Enter social media, which has upended the social, technical, and communication contingencies upon which public opinion is constructed. This study documents how political professionals turn to social media to understand the public, charting important implications for the practice of campaigning as well as the study of public opinion itself. An analysis of in-depth interviews with 13 professionals from 2016 US presidential campaigns details how they use social media to understand and represent public opinion. I map these uses of social media onto a theoretical model, accounting for quantitative and qualitative measurement, for instrumental and symbolic purposes. Campaigns’ use of social media data to infer and symbolize public opinion is a new development in the relationship between campaigns and supporters. These new tools and symbols of public opinion are shaped by campaigns and drive press coverage (McGregor 2019), highlighting the hybrid logic of the political media system (Chadwick 2017). The model I present brings much-needed attention to qualitative data, a novel aspect of social media in understanding public opinion. The use of social media data to understand the public, for all its problems of representativeness, may provide a retort to long-standing criticisms of surveys—specifically that surveys do not reveal hierarchical, social, or public aspects of opinion formation (Blumer 1948; Herbst 1998; Cramer 2016). This model highlights a need to explicate what can—and cannot—be understood about public opinion via social media.

scholarly journals Embed2Detect: temporally clustered embedded words for event detection in social media

2021 ◽  
Author(s):  
Hansi Hettiarachchi ◽  
Mariam Adedoyin-Olowe ◽  
Jagdev Bhogal ◽  
Mohamed Medhat Gaber
Keyword(s):  
Social Media ◽  
Event Detection ◽  
High Volume ◽  
Detection Methods ◽  
Word Embeddings ◽  
Agglomerative Clustering ◽  
Data Set ◽  
Social Media Data ◽  
Social Media Platforms ◽  
Media Data

AbstractSocial media is becoming a primary medium to discuss what is happening around the world. Therefore, the data generated by social media platforms contain rich information which describes the ongoing events. Further, the timeliness associated with these data is capable of facilitating immediate insights. However, considering the dynamic nature and high volume of data production in social media data streams, it is impractical to filter the events manually and therefore, automated event detection mechanisms are invaluable to the community. Apart from a few notable exceptions, most previous research on automated event detection have focused only on statistical and syntactical features in data and lacked the involvement of underlying semantics which are important for effective information retrieval from text since they represent the connections between words and their meanings. In this paper, we propose a novel method termed Embed2Detect for event detection in social media by combining the characteristics in word embeddings and hierarchical agglomerative clustering. The adoption of word embeddings gives Embed2Detect the capability to incorporate powerful semantical features into event detection and overcome a major limitation inherent in previous approaches. We experimented our method on two recent real social media data sets which represent the sports and political domain and also compared the results to several state-of-the-art methods. The obtained results show that Embed2Detect is capable of effective and efficient event detection and it outperforms the recent event detection methods. For the sports data set, Embed2Detect achieved 27% higher F-measure than the best-performed baseline and for the political data set, it was an increase of 29%.

Google Plus as a Contentious Field of Revolutionary Identity

2021 ◽  
Vol 20 (3) ◽  
pp. 402-416
Author(s):  
Amirhossein Teimouri
Keyword(s):  
Social Media ◽  
Iranian Revolution ◽  
Social Media Data ◽  
Social Media Platforms ◽  
New Generation ◽  
Media Data

Abstract Social media platforms have been increasingly reinvigorating extreme movements, especially rightist movements. Utilizing unique Google Plus data, the author shows the rise and fall of the 2015 rightist anti-Nuclear Deal movement in Iran. He argues that the Google Plus platform in 2015 provided the new generation of revolutionary Islamist rightist activists with a contentious space of mobilization, enabling them to develop a new revolutionary rightist identity. This revolutionary identity and its corresponding language and discourse did not fully unfold in Iranian mainstream rightist media, even though rightist groups, compared to liberal groups, are not censored and repressed. The new generation of rightist activists perceived the Nuclear Deal as an existential threat to revolutionary principles of the country, and thus played out their outrage and identity anxieties on Google Plus. The author contends that this online outrage, due to the activists’ identity bond with the regime and the 1979 Iranian Revolution, however, did not translate into any massive offline mobilization against the Nuclear Deal. He also discusses the methodological implications of using social media data, especially the discontinuation of Google Plus.

scholarly journals Mining Social Media Data for Biomedical Signals and Health-Related Behavior

2020 ◽  
Vol 3 (1) ◽  
pp. 433-458 ◽  
Author(s):  
Rion Brattig Correia ◽  
Ian B. Wood ◽  
Johan Bollen ◽  
Luis M. Rocha
Keyword(s):  
Mental Health ◽  
Social Media ◽  
Population Level ◽  
Data Access ◽  
Health Conditions ◽  
Social Phenomena ◽  
Medical Treatments ◽  
Social Media Data ◽  
Health Related ◽  
Media Data

Social media data have been increasingly used to study biomedical and health-related phenomena. From cohort-level discussions of a condition to population-level analyses of sentiment, social media have provided scientists with unprecedented amounts of data to study human behavior associated with a variety of health conditions and medical treatments. Here we review recent work in mining social media for biomedical, epidemiological, and social phenomena information relevant to the multilevel complexity of human health. We pay particular attention to topics where social media data analysis has shown the most progress, including pharmacovigilance and sentiment analysis, especially for mental health. We also discuss a variety of innovative uses of social media data for health-related applications as well as important limitations of social media data access and use.

scholarly journals Using social media data to map the areas most affected by ISIS in Syria

2020 ◽  
Vol 26 (1) ◽  
pp. 464-470
Author(s):  
Mohamad Hasan
Keyword(s):  
Social Media ◽  
Language Processing ◽  
Geographical Information ◽  
Data Mapping ◽  
Islamic State ◽  
Social Media Data ◽  
The Social ◽  
Mapping Process ◽  
Data Source ◽  
Media Data

This paper presents a model to collect, save, geocode, and analyze social media data. The model is used to collect and process the social media data concerned with the ISIS terrorist group (the Islamic State in Iraq and Syria), and to map the areas in Syria most affected by ISIS accordingly to the social media data. Mapping process is assumed automated compilation of a density map for the geocoded tweets. Data mined from social media (e.g., Twitter and Facebook) is recognized as dynamic and easily accessible resources that can be used as a data source in spatial analysis and geographical information system. Social media data can be represented as a topic data and geocoding data basing on the text of the mined from social media and processed using Natural Language Processing (NLP) methods. NLP is a subdomain of artificial intelligence concerned with the programming computers to analyze natural human language and texts. NLP allows identifying words used as an initial data by developed geocoding algorithm. In this study, identifying the needed words using NLP was done using two corpora. First corpus contained the names of populated places in Syria. The second corpus was composed in result of statistical analysis of the number of tweets and picking the words that have a location meaning (i.e., schools, temples, etc.). After identifying the words, the algorithm used Google Maps geocoding API in order to obtain the coordinates for posts.

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