  Although the effects of the social norm on mitigating misinformation are
identified, scant knowledge exists about the patterns of social norm emergence,
such as the patterns and variations of social tipping in online communities
with diverse characteristics. Accordingly, this study investigates the features
of social tipping in online communities and examines the correlations between
the tipping features and characteristics of online communities. Taking the side
effects of COVID-19 vaccination as the case topic, we first track the patterns
of tipping features in 100 online communities, which are detected using Louvain
Algorithm from the aggregated communication network on Twitter between May 2020
and April 2021. Then, we use multi-variant linear regression to explore the
correlations between tipping features and community characteristics. We find
that social tipping in online communities can sustain for two to four months
and lead to a 50% increase in populations who accept the normative belief in
online communities. The regression indicates that the duration of social
tipping is positively related to the community populations and original
acceptance of social norms, while the correlation between the tipping duration
and the degrees among community members is negative. Additionally, the network
modularity and original acceptance of social norms have negative relationships
with the extent of social tipping, while the degree and betweenness centrality
can have significant positive relationships with the extent of tipping. Our
findings shed light on more precise normative interventions on misinformation
in digital environments as it offers preliminary evidence about the timing and
mechanism of social norm emergence.
