TY - JOUR
T1 - Themes, communities and influencers of online probiotics chatter: A retrospective analysis from 2009-2017
AU - Vijaykumar, Santosh
AU - Raamkumar, Aravind Sesagiri
AU - McCarty, Kristofor
AU - Mutumbwa, Cuthbert
AU - Mustafa, Jawwad
AU - Au, Cyndy
N1 - Funding information:
This project was funded by the Consumer Data Research Centre, an ESRC Data Investment, under project ID CDRC 085, ES/L011840/1; ES/L011891/1. The grant was awarded to SV (PI) and KM (Co-I).
PY - 2021/10/21
Y1 - 2021/10/21
N2 - We build on recent examinations questioning the quality of online information about probiotic products by studying the themes of content, detecting virtual communities and identifying key influencers in social media using data science techniques. We conducted topic modelling (n = 36,715 tweets) and longitudinal social network analysis (n = 17,834 tweets) of probiotic chatter on Twitter from 2009–17. We used Latent Dirichlet Allocation (LDA) to build the topic models and network analysis tool Gephi for building yearly graphs. We identified the top 10 topics of probiotics-related communication on Twitter and a constant rise in communication activity. However the number of communities grew consistently to peak in 2014 before dipping and levelling off by 2017. While several probiotics industry actors appeared and disappeared during this period, the influence of one specific actor rose from a hub initially to an authority in the latter years. With multi-brand advertising and probiotics promotions mostly occupying the Twitter chatter, scientists, journalists, or policymakers exerted minimal influence in these communities. Consistent with previous research, we find that probiotics-related content on social media veers towards promotions and benefits. Probiotic industry actors maintain consistent presence on Twitter while transitioning from hubs to authorities over time; scientific entities assume an authoritative role without much engagement. The involvement of scientific, journalistic or regulatory stakeholders will help create a balanced informational environment surrounding probiotic products.
AB - We build on recent examinations questioning the quality of online information about probiotic products by studying the themes of content, detecting virtual communities and identifying key influencers in social media using data science techniques. We conducted topic modelling (n = 36,715 tweets) and longitudinal social network analysis (n = 17,834 tweets) of probiotic chatter on Twitter from 2009–17. We used Latent Dirichlet Allocation (LDA) to build the topic models and network analysis tool Gephi for building yearly graphs. We identified the top 10 topics of probiotics-related communication on Twitter and a constant rise in communication activity. However the number of communities grew consistently to peak in 2014 before dipping and levelling off by 2017. While several probiotics industry actors appeared and disappeared during this period, the influence of one specific actor rose from a hub initially to an authority in the latter years. With multi-brand advertising and probiotics promotions mostly occupying the Twitter chatter, scientists, journalists, or policymakers exerted minimal influence in these communities. Consistent with previous research, we find that probiotics-related content on social media veers towards promotions and benefits. Probiotic industry actors maintain consistent presence on Twitter while transitioning from hubs to authorities over time; scientific entities assume an authoritative role without much engagement. The involvement of scientific, journalistic or regulatory stakeholders will help create a balanced informational environment surrounding probiotic products.
KW - Advertising/methods
KW - Humans
KW - Longitudinal Studies
KW - Probiotics/administration & dosage
KW - Retrospective Studies
KW - Social Media/statistics & numerical data
UR - http://www.scopus.com/inward/record.url?scp=85117848140&partnerID=8YFLogxK
U2 - 10.1371/journal.pone.0258098
DO - 10.1371/journal.pone.0258098
M3 - Article
C2 - 34673767
SN - 1932-6203
VL - 16
JO - PLoS One
JF - PLoS One
IS - 10
M1 - e0258098
ER -