scholarly journals Classification of Health-Related Social Media Posts: Evaluation of Post Content–Classifier Models and Analysis of User Demographics (Preprint)

2019 ◽  
Author(s):  
Ryan Rivas ◽  
Shouq A Sadah ◽  
Yuhang Guo ◽  
Vagelis Hristidis
Keyword(s):  
Social Media ◽  
Online Social Networks ◽  
Research Question ◽  
Age Group ◽  
Online Forums ◽  
Demographic Groups ◽  
Shared Experiences ◽  
Different Types ◽  
Health Related

BACKGROUND The increasing volume of health-related social media activity, where users connect, collaborate, and engage, has increased the significance of analyzing how people use health-related social media. OBJECTIVE The aim of this study was to classify the content (eg, posts that share experiences and seek support) of users who write health-related social media posts and study the effect of user demographics on post content. METHODS We analyzed two different types of health-related social media: (1) health-related online forums—WebMD and DailyStrength—and (2) general online social networks—Twitter and Google+. We identified several categories of post content and built classifiers to automatically detect these categories. These classifiers were used to study the distribution of categories for various demographic groups. RESULTS We achieved an accuracy of at least 84% and a balanced accuracy of at least 0.81 for half of the post content categories in our experiments. In addition, 70.04% (4741/6769) of posts by male WebMD users asked for advice, and male users’ WebMD posts were more likely to ask for medical advice than female users’ posts. The majority of posts on DailyStrength shared experiences, regardless of the gender, age group, or location of their authors. Furthermore, health-related posts on Twitter and Google+ were used to share experiences less frequently than posts on WebMD and DailyStrength. CONCLUSIONS We studied and analyzed the content of health-related social media posts. Our results can guide health advocates and researchers to better target patient populations based on the application type. Given a research question or an outreach goal, our results can be used to choose the best online forums to answer the question or disseminate a message.

scholarly journals Classification of Health-Related Social Media Posts: Evaluation of Post Content–Classifier Models and Analysis of User Demographics

10.2196/14952 ◽  
2020 ◽  
Vol 6 (2) ◽  
pp. e14952 ◽  
Author(s):  
Ryan Rivas ◽  
Shouq A Sadah ◽  
Yuhang Guo ◽  
Vagelis Hristidis
Keyword(s):  
Social Media ◽  
Online Social Networks ◽  
Research Question ◽  
Age Group ◽  
Online Forums ◽  
Demographic Groups ◽  
Shared Experiences ◽  
Different Types ◽  
Health Related

Background The increasing volume of health-related social media activity, where users connect, collaborate, and engage, has increased the significance of analyzing how people use health-related social media. Objective The aim of this study was to classify the content (eg, posts that share experiences and seek support) of users who write health-related social media posts and study the effect of user demographics on post content. Methods We analyzed two different types of health-related social media: (1) health-related online forums—WebMD and DailyStrength—and (2) general online social networks—Twitter and Google+. We identified several categories of post content and built classifiers to automatically detect these categories. These classifiers were used to study the distribution of categories for various demographic groups. Results We achieved an accuracy of at least 84% and a balanced accuracy of at least 0.81 for half of the post content categories in our experiments. In addition, 70.04% (4741/6769) of posts by male WebMD users asked for advice, and male users’ WebMD posts were more likely to ask for medical advice than female users’ posts. The majority of posts on DailyStrength shared experiences, regardless of the gender, age group, or location of their authors. Furthermore, health-related posts on Twitter and Google+ were used to share experiences less frequently than posts on WebMD and DailyStrength. Conclusions We studied and analyzed the content of health-related social media posts. Our results can guide health advocates and researchers to better target patient populations based on the application type. Given a research question or an outreach goal, our results can be used to choose the best online forums to answer the question or disseminate a message.

Tracking self-reported symptoms and medical conditions on social media during the COVID-19 pandemic (Preprint)

2021 ◽  
Author(s):  
Qinglan Ding ◽  
Daisy Massey ◽  
Chenxi Huang ◽  
Connor Grady ◽  
Yuan Lu ◽  
...  
Keyword(s):  
Mental Health ◽  
Infectious Disease ◽  
Social Media ◽  
Positive Predictive Value ◽  
Medical Condition ◽  
Medical Conditions ◽  
Health Related ◽  
The U.S ◽  
Over Time

BACKGROUND Harnessing health-related data posted on social media in real-time has the potential to offer insights into how the pandemic impacts the mental health and general well-being of individuals and populations over time. OBJECTIVE The aim of this study was to obtain information on symptoms and medical conditions self-reported by non-Twitter social media users during the coronavirus disease 2019 (COVID-19) pandemic, and to determine how discussion of these symptoms and medical conditions on social media changed over time. METHODS We used natural language processing (NLP) algorithms to identify symptom and medical condition topics being discussed on social media between June 14 and December 13, 2020. The sample social media posts were geotagged by NetBase, a third-party data provider. We calculated the positive predictive value and sensitivity to validate the classification of the posts. We also assessed the frequency of different health-related discussions on social media over time during the study period, and compared the changes in the frequency of each symptom/medical condition discussion to the fluctuation of U.S. daily new COVID-19 cases during the study period. Additionally, we compared the trends of the 5 most commonly mentioned symptoms and medical conditions from June 14 to August 31 (when the U.S. passed 6 million COVID-19 cases) to the trends observed from September 1 to December 13, 2020. RESULTS Within a total of 9,807,813 posts (nearly 70% were sourced from the U.S.), we identified discussion of 120 symptom topics and 1,542 medical condition topics. Our classification of the health-related posts had a positive predictive value of over 80% and an average classification rate of 92% sensitivity. The 5 most commonly mentioned symptoms on social media during the study period were: anxiety (in 201,303 posts or 12.2% of the total posts mentioning symptoms), generalized pain (189,673, 11.5%), weight loss (95,793, 5.8%), fatigue (91,252, 5.5%), and coughing (86,235, 5.2%). The 5 most discussed medical conditions were: COVID-19 (in 5,420,276 posts or 66.4% of the total posts mentioning medical conditions), unspecified infectious disease (469,356, 5.8%), influenza (270,166, 3.3%), unspecified disorders of the central nervous system (253,407, 3.1%), and depression (151,752, 1.9%). The changes in the frequency of 2 medical conditions, COVID-19 and unspecified infectious disease, were similar to the fluctuation of daily new confirmed cases of COVID-19 in the U.S. CONCLUSIONS COVID-19 and symptoms of anxiety were the two most commonly discussed health-related topics on social media from June 14 to December 13, 2020. Real-time monitoring of social media posts on symptoms and medical conditions may help assess the population's mental health status and enhance public health surveillance for infectious disease.

Privacy in Online Social Networks

2021 ◽  
pp. 2078-2109
Author(s):  
Ramanpreet Kaur ◽  
Tomaž Klobučar ◽  
Dušan Gabrijelčič
Keyword(s):  
Social Networks ◽  
Social Media ◽  
Online Social Networks ◽  
Privacy Protection ◽  
Privacy Preservation ◽  
Mitigation Strategies ◽  
Research Issues ◽  
Privacy Requirements ◽  
Privacy Threats

This chapter is concerned with the identification of the privacy threats to provide a feedback to the users so that they can make an informed decision based on their desired level of privacy. To achieve this goal, Solove's taxonomy of privacy violations is refined to incorporate the modern challenges to the privacy posed by the evolution of social networks. This work emphasizes on the fact that the privacy protection should be a joint effort of social network owners and users, and provides a classification of mitigation strategies according to the party responsible for taking these countermeasures. In addition, it highlights the key research issues to guide the research in the field of privacy preservation. This chapter can serve as a first step to comprehend the privacy requirements of online users and educate the users about their choices and actions in social media.

Display and control in online social spaces: Towards a typology of users

2016 ◽  
Vol 20 (3) ◽  
pp. 845-861 ◽  
Author(s):  
Alexandre Fortier ◽  
Jacquelyn Burkell
Keyword(s):  
Social Networks ◽  
Social Media ◽  
Online Social Networks ◽  
Q Methodology ◽  
Personal Information ◽  
Public Spaces ◽  
Information Control ◽  
Different Types ◽  
Social Display ◽  
And Control

Earlier research using qualitative techniques suggests that the default conception of online social networks is as public spaces with little or no expectation of control over content or distribution of profile information. Some research, however, suggests that users within these spaces have different perspectives on information control and distribution. This study uses Q methodology to investigate subjective perspectives with respect to privacy of, and control over, Facebook profiles. The results suggests three different types of social media users: those who view profiles as spaces for controlled social display, exerting control over content or audience; those who treat their profiles as spaces for open social display, exercising little control over either content or audience; and those who view profiles as places to post personal information to a controlled audience. We argue that these different perspectives lead to different privacy needs and expectations.

PSYCHOLOGICAL FEATURES OF SOCIAL MEDIA USERS’ ACTIVITY

2019 ◽  
pp. 160-181
Author(s):  
O. P. Sosniuk ◽  
I. V. Оstapenko
Keyword(s):  
Social Media ◽  
Psychological Characteristics ◽  
Active Participant ◽  
Media Impact ◽  
Different Types ◽  
Definition Of ◽  
Psychological Features ◽  
Civic Competence

The article deals with the analysis of psychological features of social media users’ activity. The authors discuss the main approaches to the classification of social media, clarify the definition of this concept. The article presents the analysis of the typologies of social media users. According to the results of the qualitative study, the authors identified eight types of social media users, (considering the specifics of their activity: 1) generator of creolized content; 2) initiator of the discussion; 3) active participant in the discussion; 4) spreader of the creolized content; 5) imitator; 6) conformist; 7) observer; 8) inactive user. The psychological characteristics of the activity of these types of users of social media are identified. It has been proved that there are some differences in the ratio of different types of users for the most popular social networks (Facebook, Instagram, Twitter) and messengers (Telegram, Viber). It is determined that the leading types of users are: for the Facebook – discussion initiator, active discussion participant, conformist; for the Instagram – generator of creolized content, spreader of the creolized content, and a follower; for the Twitter – generator of creolized content, spreader of the creolized content and a discussion initiator; for Telegram – discussion initiator, active discussion participant, spreader of the creolized content; for Viber messenger – initiator of discussions, active participant of discussions, conformist. The prospects for further research are outlined: verification of the typology of social media users in an expanded sample, specification of the psychological profile of different types of social media users, creation of technologies for development of personality’s media competence, identification of preconditions for constructive social media impact on users, exploration of the role of social media in the process of building a personality’s civic competence.

scholarly journals Coronajerga, covidioma, coronalengua: acerca de los cambios lingüísticos en tiempos de la pandemia

2021 ◽  
Vol 33 ◽  
pp. 1-26
Author(s):  
Agnieszka Gwiazdowska
Keyword(s):  
Social Media ◽  
Formation Process ◽  
Theoretical Framework ◽  
Word Formation ◽  
Formation Mechanisms ◽  
Lexical Borrowing ◽  
Base Form ◽  
Different Types ◽  
The Way

The aim of this paper is to present how the worldwide COVID-19 pandemic has changed our language and the way we communicate. The article focuses on the recent Spanish neologisms that have appeared during the pandemic year 2020 and attempts to analyze their word-formation process. The theoretical framework of this study is based on the classification of neologisms proposed by M.T. Cabré Castellví (2006). Firstly, the paper highlights semantic innovations, that is, neologisms which are formed through broadening, narrowing or change of the meaning of the base form. Secondly, different types of word formation mechanisms, such as affixations, compounding, conversion or shortening are discussed. The paper also gives new insights into the most creative ways that vocabulary related to coronavirus (COVID-19) has expanded (lexical borrowing, wordplay). The data were collected from articles, books, dictionaries, social media and various websites.

scholarly journals A Framework for Detecting Intentions of Criminal Acts in Social Media: A Case Study on Twitter

Information ◽  
2020 ◽  
Vol 11 (3) ◽  
pp. 154 ◽  
Author(s):  
Ricardo Resende de Mendonça ◽  
Daniel Felix de Brito ◽  
Ferrucio de Franco Rosa ◽  
Júlio Cesar dos Reis ◽  
Rodrigo Bonacin
Keyword(s):  
Machine Learning ◽  
Social Networks ◽  
Social Media ◽  
Social Network ◽  
Online Social Networks ◽  
Communication Planning ◽  
Flexible Model ◽  
Selection Of

Criminals use online social networks for various activities by including communication, planning, and execution of criminal acts. They often employ ciphered posts using slang expressions, which are restricted to specific groups. Although literature shows advances in analysis of posts in natural language messages, such as hate discourses, threats, and more notably in the sentiment analysis; research enabling intention analysis of posts using slang expressions is still underexplored. We propose a framework and construct software prototypes for the selection of social network posts with criminal slang expressions and automatic classification of these posts according to illocutionary classes. The developed framework explores computational ontologies and machine learning (ML) techniques. Our defined Ontology of Criminal Expressions represents crime concepts in a formal and flexible model, and associates them with criminal slang expressions. This ontology is used for selecting suspicious posts and decipher them. In our solution, the criminal intention in written posts is automatically classified relying on learned models from existing posts. This work carries out a case study to evaluate the framework with 8,835,290 tweets. The obtained results show its viability by demonstrating the benefits in deciphering posts and the effectiveness of detecting user’s intention in written criminal posts based on ML.

scholarly journals Greater Internet use is not associated with faster growth in political polarization among US demographic groups

2017 ◽  
Vol 114 (40) ◽  
pp. 10612-10617 ◽  
Author(s):  
Levi Boxell ◽  
Matthew Gentzkow ◽  
Jesse M. Shapiro
Keyword(s):  
Social Media ◽  
Linear Model ◽  
Internet Use ◽  
Political Polarization ◽  
The Internet ◽  
Age Group ◽  
Group Level ◽  
Demographic Groups ◽  
Recent Growth ◽  
The Individual

We combine eight previously proposed measures to construct an index of political polarization among US adults. We find that polarization has increased the most among the demographic groups least likely to use the Internet and social media. Our overall index and all but one of the individual measures show greater increases for those older than 65 than for those aged 18–39. A linear model estimated at the age-group level implies that the Internet explains a small share of the recent growth in polarization.

scholarly journals Tracking Self-reported Symptoms and Medical Conditions on Social Media During the COVID-19 Pandemic: Infodemiological Study

10.2196/29413 ◽  
2021 ◽  
Vol 7 (9) ◽  
pp. e29413
Author(s):  
Qinglan Ding ◽  
Daisy Massey ◽  
Chenxi Huang ◽  
Connor B Grady ◽  
Yuan Lu ◽  
...  
Keyword(s):  
Infectious Disease ◽  
United States ◽  
Social Media ◽  
Weight Loss ◽  
Medical Condition ◽  
The United States ◽  
Medical Conditions ◽  
Health Related ◽  
Over Time

Background Harnessing health-related data posted on social media in real time can offer insights into how the pandemic impacts the mental health and general well-being of individuals and populations over time. Objective This study aimed to obtain information on symptoms and medical conditions self-reported by non-Twitter social media users during the COVID-19 pandemic, to determine how discussion of these symptoms and medical conditions changed over time, and to identify correlations between frequency of the top 5 commonly mentioned symptoms post and daily COVID-19 statistics (new cases, new deaths, new active cases, and new recovered cases) in the United States. Methods We used natural language processing (NLP) algorithms to identify symptom- and medical condition–related topics being discussed on social media between June 14 and December 13, 2020. The sample posts were geotagged by NetBase, a third-party data provider. We calculated the positive predictive value and sensitivity to validate the classification of posts. We also assessed the frequency of health-related discussions on social media over time during the study period, and used Pearson correlation coefficients to identify statistically significant correlations between the frequency of the 5 most commonly mentioned symptoms and fluctuation of daily US COVID-19 statistics. Results Within a total of 9,807,813 posts (nearly 70% were sourced from the United States), we identified a discussion of 120 symptom-related topics and 1542 medical condition–related topics. Our classification of the health-related posts had a positive predictive value of over 80% and an average classification rate of 92% sensitivity. The 5 most commonly mentioned symptoms on social media during the study period were anxiety (in 201,303 posts or 12.2% of the total posts mentioning symptoms), generalized pain (189,673, 11.5%), weight loss (95,793, 5.8%), fatigue (91,252, 5.5%), and coughing (86,235, 5.2%). The 5 most discussed medical conditions were COVID-19 (in 5,420,276 posts or 66.4% of the total posts mentioning medical conditions), unspecified infectious disease (469,356, 5.8%), influenza (270,166, 3.3%), unspecified disorders of the central nervous system (253,407, 3.1%), and depression (151,752, 1.9%). Changes in posts in the frequency of anxiety, generalized pain, and weight loss were significant but negatively correlated with daily new COVID-19 cases in the United States (r=-0.49, r=-0.46, and r=-0.39, respectively; P<.05). Posts on the frequency of anxiety, generalized pain, weight loss, fatigue, and the changes in fatigue positively and significantly correlated with daily changes in both new deaths and new active cases in the United States (r ranged=0.39-0.48; P<.05). Conclusions COVID-19 and symptoms of anxiety were the 2 most commonly discussed health-related topics on social media from June 14 to December 13, 2020. Real-time monitoring of social media posts on symptoms and medical conditions may help assess the population’s mental health status and enhance public health surveillance for infectious disease.

Privacy in Online Social Networks

2021 ◽  
pp. 567-598
Author(s):  
Ramanpreet Kaur ◽  
Tomaž Klobučar ◽  
Dušan Gabrijelčič
Keyword(s):  
Social Networks ◽  
Social Media ◽  
Online Social Networks ◽  
Privacy Protection ◽  
Privacy Preservation ◽  
Mitigation Strategies ◽  
Research Issues ◽  
Privacy Requirements ◽  
Privacy Threats

This chapter is concerned with the identification of the privacy threats to provide a feedback to the users so that they can make an informed decision based on their desired level of privacy. To achieve this goal, Solove's taxonomy of privacy violations is refined to incorporate the modern challenges to the privacy posed by the evolution of social networks. This work emphasizes on the fact that the privacy protection should be a joint effort of social network owners and users, and provides a classification of mitigation strategies according to the party responsible for taking these countermeasures. In addition, it highlights the key research issues to guide the research in the field of privacy preservation. This chapter can serve as a first step to comprehend the privacy requirements of online users and educate the users about their choices and actions in social media.

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