Data analysis
Manual encoding of Twitter content is more accurate than automatic
encoding because humans can perceive linguistically refined text more
efficiently than computer-based systems.15 Therefore,
data were recorded and evaluated manually in this study. The data were
recorded using the Microsoft Excel program and qualitatively evaluated
by thematic analysis16 and the themes were determined
based on this analysis.
The tweets were recorded under the following codes: (1) date, (2) number
of retweets, (3) number of likes, (4) tweet source, (5) tweet, (6)
theme, (7) positive/negative/neutral, (8) tweet uploader status. The
tweets were posted by 3 different groups of people: (1) community, (2)
layperson, (3) news. 5 themes were identified: (1) criticism of
bullying, (2) news about bullying in CLP, (3) parental experience of a
child being bullied, (4) personal experience of being bullied, (5)
social support against bullying. Tweet uploader status was classified
into 3 groups: (1) CLP subjects, (2) CLP subjects’ parents, (3)
irrelevant individuals (individuals who were unaffected). All tweets
were evaluated by 2 experienced orthodontists who worked independently.
When a case of conflict existed, two investigators exchanged ideas and
determine the final theme together after the first evaluation.