Replication Data for: Who Gets a Say in This? Speaking Security on Social Media
<|begin_of_text|><|start_header_id|>system<|end_header_id|> You are a research archivist expert in dataset metadata. Answer the question truthfully.  <|eot_id|><|start_header_id|>user<|end_header_id|> Here is the Description of a dataset. Description:'Can social media revert the top-down dynamics of securitization? Limited by the notion that ‘security is only articulated in an institutional voice by the elites’, the role of non-elite actors has remained understudied. Only recently has it been proposed that lay actors can become influential security agents through their online activity. However, social media’s capacity to revert the top-down dynamics of securitization remains contended. To explore this puzzle and seeking to update the theory of securitization to the modern context of political communication, this study employs a semi-supervised machine learning approach to analyse a novel dataset of over 10 million Twitter messages by five elite and non-elite actor groups discussing the Amazon rainforest fires in 2019. Finally, the study uses vector autoregression (VAR) models to expl' . Please return only a json list of at most 3 Keywords corresponding to the Description:.<|eot_id|><|start_header_id|>assistant<|end_header_id|> {"Answer" : ["International security, political communication, quantitative text analysis, social media"]} <|eot_id|>
 Here is the Description of a dataset. Description:'Can social media revert the top-down dynamics of securitization? Limited by the notion that ‘security is only articulated in an institutional voice by the elites’, the role of non-elite actors has remained understudied. Only recently has it been proposed that lay actors can become influential security agents through their online activity. However, social media’s capacity to revert the top-down dynamics of securitization remains contended. To explore this puzzle and seeking to update the theory of securitization to the modern context of political communication, this study employs a semi-supervised machine learning approach to analyse a novel dataset of over 10 million Twitter messages by five elite and non-elite actor groups discussing the Amazon rainforest fires in 2019. Finally, the study uses vector autoregression (VAR) models to expl' . Please return only a json list of at most 3 Keywords corresponding to the Description:.
Can social media revert the top-down dynamics of securitization? Limited by the notion that ‘security is only articulated in an institutional voice by the elites’, the role of non-elite actors has remained understudied. Only recently has it been proposed that lay actors can become influential security agents through their online activity. However, social media’s capacity to revert the top-down dynamics of securitization remains contended. To explore this puzzle and seeking to update the theory of securitization to the modern context of political communication, this study employs a semi-supervised machine learning approach to analyse a novel dataset of over 10 million Twitter messages by five elite and non-elite actor groups discussing the Amazon rainforest fires in 2019. Finally, the study uses vector autoregression (VAR) models to expl
International security, political communication, quantitative text analysis, social media