Document Type

Poster

Start Date

26-6-2024 11:00 AM

End Date

26-6-2024 1:00 PM

Description

Challenges in Social Media Text Analysis Data Abnormalities: •Ideological datasets often have excessive zeros, nonnormal distributions, and semi-continuous data, complicating accurate analysis, particularly for rare phenomena like radical language and hate speech (King & Zeng, 2001; Wiegand et al., 2019) . Traditional Methods' Limitations: •Existing linguistic processing tools (e.g., LIWC, WordNet) struggle with dynamic and context-dependent language on platforms like Twitter (Tausczik & Pennebaker, 2010; Boyd, 2017). The above challenges are further magnified in studies involving sensitive topics like terrorism-related content, where the extremely low base rates of relevant terms often lead to potential biases in analytical results (Conway et al., 2012; Scrivens et al., 2020).

Comments

Scientific Innovation Award

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Jun 26th, 11:00 AM Jun 26th, 1:00 PM

Navigating Social Media Text Analytics: Overcoming Linguistic Complexity via Advanced Modeling Techniques

Challenges in Social Media Text Analysis Data Abnormalities: •Ideological datasets often have excessive zeros, nonnormal distributions, and semi-continuous data, complicating accurate analysis, particularly for rare phenomena like radical language and hate speech (King & Zeng, 2001; Wiegand et al., 2019) . Traditional Methods' Limitations: •Existing linguistic processing tools (e.g., LIWC, WordNet) struggle with dynamic and context-dependent language on platforms like Twitter (Tausczik & Pennebaker, 2010; Boyd, 2017). The above challenges are further magnified in studies involving sensitive topics like terrorism-related content, where the extremely low base rates of relevant terms often lead to potential biases in analytical results (Conway et al., 2012; Scrivens et al., 2020).