Dataset Information

Evidence of complex contagion of information in social media: An experiment using Twitter bots.

ABSTRACT: It has recently become possible to study the dynamics of information diffusion in techno-social systems at scale, due to the emergence of online platforms, such as Twitter, with millions of users. One question that systematically recurs is whether information spreads according to simple or complex dynamics: does each exposure to a piece of information have an independent probability of a user adopting it (simple contagion), or does this probability depend instead on the number of sources of exposure, increasing above some threshold (complex contagion)? Most studies to date are observational and, therefore, unable to disentangle the effects of confounding factors such as social reinforcement, homophily, limited attention, or network community structure. Here we describe a novel controlled experiment that we performed on Twitter using 'social bots' deployed to carry out coordinated attempts at spreading information. We propose two Bayesian statistical models describing simple and complex contagion dynamics, and test the competing hypotheses. We provide experimental evidence that the complex contagion model describes the observed information diffusion behavior more accurately than simple contagion. Future applications of our results include more effective defenses against malicious propaganda campaigns on social media, improved marketing and advertisement strategies, and design of effective network intervention techniques.

SUBMITTER: Monsted B

PROVIDER: S-EPMC5609738 | biostudies-literature | 2017

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Evidence of complex contagion of information in social media: An experiment using Twitter bots.

Mønsted Bjarke B Sapieżyński Piotr P Ferrara Emilio E Lehmann Sune S

PloS one 20170922 9

It has recently become possible to study the dynamics of information diffusion in techno-social systems at scale, due to the emergence of online platforms, such as Twitter, with millions of users. One question that systematically recurs is whether information spreads according to simple or complex dynamics: does each exposure to a piece of information have an independent probability of a user adopting it (simple contagion), or does this probability depend instead on the number of sources of expo ...[more]

PMID: 28937984

Similar Datasets

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.

Project description:Importance:Little is known about whether the substantial clustering of obesity and overweight within social and geographic networks results from causal pathways, such as social contagion and shared environments, or from self-selection. Objectives:This study aimed to determine whether exposure to communities with higher rates of obesity increases the body mass index (BMI) of individuals, calculated as weight in kilograms divided by height in meters squared, and their risk of being overweight or obese, and whether social contagion, shared environments, or self-selection can account for identified differences. Design, Setting, and Participants:This natural experiment study used the routine assignment of military service members to installations as a source of exogenous variation in exposure to communities with higher vs lower rates of obesity. The study, which used data collected by the Military Teenagers' Environments, Exercise, and Nutrition Study, examined families from 38 military installations around the United States to determine if individuals had higher BMI and greater odds of overweight and obesity when assigned to installations in counties with higher rates of obesity. The study also examined if the relationship persisted after controlling for shared built environments. The participants included 1 parent and 1 child aged 12 or 13 years from 1519 families of Army-enlisted personnel. Data analysis was completed from November 2016 to October 2017. Exposures:Adult obesity rate in the county where the assigned installation of the service member was located. Time at installation and location of residence (on-installation vs off-installation) were used to measure the degree of exposure. Main Outcomes and Measures:For parents, outcomes were BMI, overweight/obesity (BMI, ≥25) and obesity (BMI, ≥30). For children, outcomes were BMI z score, overweight/obesity (BMI percentile for age and sex, ≥85), and obesity (BMI percentile for age and sex, ≥95). These outcomes were based on self-reports for parents, self-reports and parent reports for all children, and anthropometric measurements for a subsample of children. Results:Members of 1519 families participated, including 1314 adults (of whom 740, or 56%, were fathers) and 1111 children (of whom 576, or 52%, were boys); anthropometric measurements were performed on 458 children. The sample was 40% white, 22% black, 24% Hispanic, and 14% other races/ethnicities. A 1-percentage point higher county obesity rate was associated with a higher BMI (a difference of 0.08; 95% CI, 0.02-0.13) and greater odds of obesity (adjusted odds ratio [aOR], 1.05; 95% CI, 1.02-1.08) in parents, and a higher BMI z score (0.01; 95% CI, 0.003-0.02) and greater odds of overweight/obesity (aOR, 1.04; 95% CI, 1.01-1.06) in children. The evidence supported stronger associations among families with more time at installation and off-installation residence. Associations persisted even after controlling for shared built environments. Conclusions and Relevance:Exposure to counties with higher rates of obesity was associated with higher BMI and higher odds of overweight and/or obesity in parents and children. There was no evidence to support self-selection or shared built environments as possible explanations, which suggests the presence of social contagion in obesity.

Project description:BACKGROUND:Respecting patient privacy and confidentiality is critical for doctor-patient relationships and public trust in medical professionals. The frequency of potentially identifiable disclosures online during periods of active engagement is unknown. OBJECTIVE:The objective of this study was to quantify potentially identifiable content shared on social media by physicians and other health care providers using the hashtag #ShareAStoryInOneTweet. METHODS:We accessed and searched Twitter's API using Symplur software for tweets that included the hashtag #ShareAStoryInOneTweet. We identified 1206 tweets by doctors, nurses, and other health professionals out of 43,374 tweets shared in May 2018. Tweet content was evaluated in January 2019 to determine the incidence of instances where names or potentially identifiable information about patients were shared; content analysis of tweets in which information about others had been disclosed was performed. The study also evaluated whether participants raised concerns about privacy breaches and estimated the frequency of deleted tweets. The study used dual, blinded coding for a 10% sample to estimate intercoder reliability using Cohen ? statistic for identifying the potential identifiability of tweet content. RESULTS:Health care professionals (n=656) disclosing information about others included 486 doctors (74.1%) and 98 nurses (14.9%). Health care professionals sharing stories about patient care disclosed the time frame in 95 tweets (95/754, 12.6%) and included patient names in 15 tweets (15/754, 2.0%). It is estimated that friends or families could likely identify the clinical scenario described in 242 of the 754 tweets (32.1%). Among 348 tweets about potentially living patients, it was estimated that 162 (46.6%) were likely identifiable by patients. Intercoder reliability in rating the potential identifiability demonstrated 86.8% agreement, with a Cohen ? of 0.8 suggesting substantial agreement. We also identified 78 out of 754 tweets (6.5%) that had been deleted on the website but were still viewable in the analytics software data set. CONCLUSIONS:During periods of active sharing online, nurses, physicians, and other health professionals may sometimes share more information than patients or families might expect. More study is needed to determine whether similar events arise frequently and to understand how to best ensure that patients' rights are adequately respected.

Dataset Information

Evidence of complex contagion of information in social media: An experiment using Twitter bots.

Publications

Evidence of complex contagion of information in social media: An experiment using Twitter bots.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets