Despite the crucial role of adolescence in human development, little research has explored how the general public perceives this stage of life. Existing research has primarily examined individual perceptions of adolescence and focused on the negative and stereotyped perceptions based on the storm-and-stress view of adolescence. The current study employed both theory-driven and data-driven approaches enabled by machine learning and big data analytics to understand public perceptions of adolescence on Twitter. I extracted millions of publicly available tweets that discussed adolescence in 2019 on Twitter, and employed word embeddings, a neural network algorithm, to analyze the latent meanings of different words in the data as reflecting public perceptions. I then analyzed the outputs of word embeddings to identify public perceptions of adolescence for different terms of adolescence as well as different subgroups of adolescents based on race/ethnicity, gender, and sexual orientation. A unique mixed-methods analytic approach was developed, in which I first leveraged the quantitative analysis of word embeddings to generate descriptors that were closely associated with adolescence, and then conducted qualitative content analysis of the descriptors while referencing the original tweets. Results suggested that adolescence is a complex and comprehensive construct that cannot be captured by a single item. While certain terms of adolescence such as teen was perceived as negative and stereotypical, the overall construct of adolescence was perceived as neutral or positive. Both similarities and differences existed in the perceptions of different subgroups of adolescents, with certain issues being identified such as the lack of voices in anti-racism movements for Asian adolescents. My dissertation can also inform future practices to promote positive framing of adolescence and create a better digital environment for adolescent development.
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