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  4. LOCO: The 88-million-word language of conspiracy corpus.

LOCO: The 88-million-word language of conspiracy corpus.

Author(s)
Miani, Alessandro  
Faculté des sciences  
Hills, Thomas
Bangerter, Adrian  
Chaire de psychologie du travail  
Date issued
August 1, 2022
In
Behavior research methods
Vol
54
No
4
From page
1794
To page
1817
Subjects
■■■
Abstract
The spread of online conspiracy theories represents a serious threat to society. To understand the content of conspiracies, here we present the language of conspiracy (LOCO) corpus. LOCO is an 88-million-token corpus composed of topic-matched conspiracy (N = 23,937) and mainstream (N = 72,806) documents harvested from 150 websites. Mimicking internet user behavior, documents were identified using Google by crossing a set of seed phrases with a set of websites. LOCO is hierarchically structured, meaning that each document is cross-nested within websites (N = 150) and topics (N = 600, on three different resolutions). A rich set of linguistic features (N = 287) and metadata includes upload date, measures of social media engagement, measures of website popularity, size, and traffic, as well as political bias and factual reporting annotations. We explored LOCO's features from different perspectives showing that documents track important societal events through time (e.g., Princess Diana's death, Sandy Hook school shooting, coronavirus outbreaks), while patterns of lexical features (e.g., deception, power, dominance) overlap with those extracted from online social media communities dedicated to conspiracy theories. By computing within-subcorpus cosine similarity, we derived a subset of the most representative conspiracy documents (N = 4,227), which, compared to other conspiracy documents, display prototypical and exaggerated conspiratorial language and are more frequently shared on Facebook. We also show that conspiracy website users navigate to websites via more direct means than mainstream users, suggesting confirmation bias. LOCO and related datasets are freely available at https://osf.io/snpcg/ .
Publication type
journal article
Identifiers
https://libra.unine.ch/handle/20.500.14713/62415
DOI
10.3758/s13428-021-01698-z
-
34697754
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