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Exploring “Mass Surveillance” Through Computational Linguistic Analysis of Five Text Corpora: Academic, Mainstream Journalism, Microblogging Hashtag Conversation, Wikipedia Articles, and Leaked Government Data
Abstract
A lot of digital ink has been spilled on the issue of “mass surveillance,” in the aftermath of the Edward Snowden mass data leak of secret government communications intelligence (COMINT) documents in 2013. To explore some of the extant ideas, five text sets were collected: academic articles, mainstream journalistic articles, Twitter microblogging messages from a #surveillance hashtag network, Wikipedia articles in the one-degree “Mass_surveillance” page network, and curated original leaked government documents. These respective text sets were analyzed with Linguistic Inquiry and Word Count (LIWC) (by Pennebaker Conglomerates, Inc.) and NVivo 11 Plus (by QSR International, Inc.). Also, the text sets were analyzed through close (human) reading (except for the government documents that were treated in a non-consumptive way). Using computational text analytics, this author found text patterns within and across the five text sets that shed light on the target topic. There were also discoveries on how textual conventions affect linguistic features and informational contents.
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