Dataset Information

Identifying Influential Factors in the Discussion Dynamics of Emerging Health Issues on Social Media: Computational Study.

ABSTRACT: BACKGROUND:Social media has become a major resource for observing and understanding public opinions using infodemiology and infoveillance methods, especially during emergencies such as disease outbreaks. For public health agencies, understanding the driving forces of web-based discussions will help deliver more effective and efficient information to general users on social media and the web. OBJECTIVE:The study aimed to identify the major contributors that drove overall Zika-related tweeting dynamics during the 2016 epidemic. In total, 3 hypothetical drivers were proposed: (1) the underlying Zika epidemic quantified as a time series of case counts; (2) sporadic but critical real-world events such as the 2016 Rio Olympics and World Health Organization's Public Health Emergency of International Concern (PHEIC) announcement, and (3) a few influential users' tweeting activities. METHODS:All tweets and retweets (RTs) containing the keyword Zika posted in 2016 were collected via the Gnip application programming interface (API). We developed an analytical pipeline, EventPeriscope, to identify co-occurring trending events with Zika and quantify the strength of these events. We also retrieved Zika case data and identified the top influencers of the Zika discussion on Twitter. The influence of 3 potential drivers was examined via a multivariate time series analysis, signal processing, a content analysis, and text mining techniques. RESULTS:Zika-related tweeting dynamics were not significantly correlated with the underlying Zika epidemic in the United States in any of the four quarters in 2016 nor in the entire year. Instead, peaks of Zika-related tweeting activity were strongly associated with a few critical real-world events, both planned, such as the Rio Olympics, and unplanned, such as the PHEIC announcement. The Rio Olympics was mentioned in >15% of all Zika-related tweets and PHEIC occurred in 27% of Zika-related tweets around their respective peaks. In addition, the overall tweeting dynamics of the top 100 most actively tweeting users on the Zika topic, the top 100 users receiving most RTs, and the top 100 users mentioned were the most highly correlated to and preceded the overall tweeting dynamics, making these groups of users the potential drivers of tweeting dynamics. The top 100 users who retweeted the most were not critical in driving the overall tweeting dynamics. There were very few overlaps among these different groups of potentially influential users. CONCLUSIONS:Using our proposed analytical workflow, EventPeriscope, we identified that Zika discussion dynamics on Twitter were decoupled from the actual disease epidemic in the United States but were closely related to and highly influenced by certain sporadic real-world events as well as by a few influential users. This study provided a methodology framework and insights to better understand the driving forces of web-based public discourse during health emergencies. Therefore, health agencies could deliver more effective and efficient web-based communications in emerging crises.

SUBMITTER: Safarnejad L

PROVIDER: S-EPMC7420635 | biostudies-literature | 2020 Jul

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Identifying Influential Factors in the Discussion Dynamics of Emerging Health Issues on Social Media: Computational Study.

Safarnejad Lida L Xu Qian Q Ge Yaorong Y Bagavathi Arunkumar A Krishnan Siddharth S Chen Shi S

JMIR public health and surveillance 20200728 3

<h4>Background</h4>Social media has become a major resource for observing and understanding public opinions using infodemiology and infoveillance methods, especially during emergencies such as disease outbreaks. For public health agencies, understanding the driving forces of web-based discussions will help deliver more effective and efficient information to general users on social media and the web.<h4>Objective</h4>The study aimed to identify the major contributors that drove overall Zika-relat ...[more]

PMID: 32348275

Similar Datasets

Project description:PurposeThe purpose of this study is to analyze the Instagram practices of current orthopaedic surgeons and the components associated with highest reach and interactions.MethodsThe top 25 orthopaedic surgeon Instagram profiles using the hashtag #ortho were ranked by the number of followers. Account information regarding followers, posts, engagement percentage, average likes, average comments, average video view, average video likes, average video comments, and estimated cost per post was recorded using social media marketing tools. An analysis of each Instagram profiles' top 10 posts, based on number of likes, was conducted. A coding framework was developed to categorized posting strategies utilized.ResultsTwenty-five Instagram accounts and 250 Instagram posts were included in the analysis. Accounts with increased engagement rating had a significantly greater number of likes and video views. When examining post characteristics that influenced the number of likes a post generated, posts that elicited negative emotions received 52.6% and 70.7% more likes than positive emotions (P = .04) and neutral emotions (P < .01), respectively. Upon assessment of posting characteristics that influenced the number of comments a post generated, promotional posts were shown to have 43.7% fewer comments than nonpromotional posts (P = .02). When evaluating factors that influenced total number of interactions a post generated, it was found that posts that asked questions generated 26.4% more interactions (P < .01) than those that do not.ConclusionsThe present investigation found that the most effective strategies to generate more interactions on Instagram are those that elicit emotional responses and provoke viewer engagement by asking questions and directing actions. Additionally, it was found that promotional content was not well received by viewers.Clinical relevanceOrthopaedic surgeons have an opportunity to connect with colleagues, patients, and interested viewers through social media platforms in order to enhance their practice, disseminate educational content, and contribute to the social media presence of orthopaedic surgery.

Project description:As e-cigarette use rapidly increases in popularity, data from online social systems (Twitter, Instagram, Google Web Search) can be used to capture and describe the social and environmental context in which individuals use, perceive, and are marketed this tobacco product. Social media data may serve as a massive focus group where people organically discuss e-cigarettes unprimed by a researcher, without instrument bias, captured in near real time and at low costs.This study documents e-cigarette-related discussions on Twitter, describing themes of conversations and locations where Twitter users often discuss e-cigarettes, to identify priority areas for e-cigarette education campaigns. Additionally, this study demonstrates the importance of distinguishing between social bots and human users when attempting to understand public health-related behaviors and attitudes.E-cigarette-related posts on Twitter (N=6,185,153) were collected from December 24, 2016, to April 21, 2017. Techniques drawn from network science were used to determine discussions of e-cigarettes by describing which hashtags co-occur (concept clusters) in a Twitter network. Posts and metadata were used to describe where geographically e-cigarette-related discussions in the United States occurred. Machine learning models were used to distinguish between Twitter posts reflecting attitudes and behaviors of genuine human users from those of social bots. Odds ratios were computed from 2x2 contingency tables to detect if hashtags varied by source (social bot vs human user) using the Fisher exact test to determine statistical significance.Clusters found in the corpus of hashtags from human users included behaviors (eg, #vaping), vaping identity (eg, #vapelife), and vaping community (eg, #vapenation). Additional clusters included products (eg, #eliquids), dual tobacco use (eg, #hookah), and polysubstance use (eg, #marijuana). Clusters found in the corpus of hashtags from social bots included health (eg, #health), smoking cessation (eg, #quitsmoking), and new products (eg, #ismog). Social bots were significantly more likely to post hashtags that referenced smoking cessation and new products compared to human users. The volume of tweets was highest in the Mid-Atlantic (eg, Pennsylvania, New Jersey, Maryland, and New York), followed by the West Coast and Southwest (eg, California, Arizona and Nevada).Social media data may be used to complement and extend the surveillance of health behaviors including tobacco product use. Public health researchers could harness these data and methods to identify new products or devices. Furthermore, findings from this study demonstrate the importance of distinguishing between Twitter posts from social bots and humans when attempting to understand attitudes and behaviors. Social bots may be used to perpetuate the idea that e-cigarettes are helpful in cessation and to promote new products as they enter the marketplace.

Dataset Information

Identifying Influential Factors in the Discussion Dynamics of Emerging Health Issues on Social Media: Computational Study.

Publications

Identifying Influential Factors in the Discussion Dynamics of Emerging Health Issues on Social Media: Computational Study.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets