Real-time Twitter interactions during World Breastfeeding Week: A case study and social network analysis

Area of Science:

• Public Health Communication
• Social Network Analysis
• Digital Health

Background:

• Public health awareness campaigns increasingly utilize Twitter for implementation.
• Current campaign evaluation relies heavily on basic Twitter Analytics, limiting insights.
• Social network theory-based metrics offer potential for enhanced understanding of campaign dynamics.

Purpose of the Study:

• To establish the potential of social network theory-based metrics in understanding public health campaigns.
• To analyze real-time user interactions on Twitter during the 2020 World Breastfeeding Week (WBW) campaign.

Main Methods:

• Employed social network analysis (SNA), including community and influencer identification, and topic modeling.
• Compared activity of 29,958 campaign participants with 10,694 reference users over a six-month pre-campaign period.
• Analyzed user interactions such as retweets and mentions.

Main Results:

• Users formed significantly more inter-connected relationships during the WBW campaign (46,161 interactions) compared to the pre-campaign period (10,662 interactions).
• Identifiable communities emerged, based on geolocation, interests, and professional backgrounds.
• Influencers from the scientific community dominated conversations, but public influencers played a crucial bridging role. Discussions expanded to include social and racial inequities in breastfeeding.

Conclusions:

• Social network analysis provides a deeper understanding of public health campaign messaging and engagement dynamics.
• Future campaigns, like WBW, could benefit from improved targeting for enhanced geographic coverage and user interaction.
• Social network theory and analysis offer valuable data on user interactions, surpassing traditional metrics for public health initiatives.