Authors:
(1) Florian Cafiero (ORCID 0000-0002-1951-6942), Sciences Po, Medialab;
(2) Jean-Baptiste Camps (ORCID 0000-0003-0385-7037), Ecole nationale des chartes, Universite Paris, Sciences & Lettres. Table of Links Abstract and Introduction Why work on QAnon? Specificities and social impact Who is Q? The theories put to test Authorship attribution Results Discussion Corpus constitution Quotes of authors outside of the corpus have been Definition of two subcorpus: dealing with generic difference and an imbalanced dataset The genre of “Q drops”: a methodological challenge Detecting style changes: rolling stylometry Ethical statement, Acknowledgements, and References Discussion If the author of the Qdrops is among our candidates, the results here seem to demonstrate the major role of Ron W. in the writing of the Qdrops, at least since the switch to 8chan. The peak of Paul F. in one of the two analyses, for the period before 8chan, could very well be revealing of a real participation, even a leading role for all the period before 8chan, with perhaps afterwards a brief period of collaboration (or competition), in between the migration to 8chan and what Paul F. himself describes as the last authentic Qdrop. Localised peaks of Christina U. or Michael F. on the other hand, while they might very hypothetically be revealing of more occasional collaborations, should probably not be over-interpreted. Given the nature of the coefficients used by the model for them (fig. 2), they seem more likely to be caused by ‘topic similarities’ due to the news and topics dealt with in the Qdrops. This could result in the choice of a similar lexicon, and even in quotations or paraphrases. Confusions between Michael F. or Christina U. on one hand, and other candidates (in training) or Q, seem due to attractions in terms of language register and generic peculiarities: samples that use a more elevated and grandiloquent type of patriotic address are brought somewhat closer to Michael F. samples, while those including heavier news-related content might be drawn towards Christina U. A more exploratory visualization of the feature space, based on correspondence analysis, shows a strong opposition (first dimension) between PaulF private posts and public writings, while the second one oppose PaulF and RonW samples (fig. 3). Projected in this feature space, the Qdrops from the 8Chan period, after the ‘board compromised’ post, mostly appear inside RonW data cloud, while previous posts, especially from the time of 4Chan are situated substantially closer to PaulF writings. This paper of course has limitations. The very nature of the Q drops, a genre in itself, and the brevity of these texts, make difficult to render a finer grain picture than the one we present here. It is thus plausible, even if we cannot demonstrate it, that other punctual interventions could have occurred. The training data we collected is important, and sufficient to obtain excellent performances. Yet, more training data could of course help increase the precision and reliability of the analyses. A media outlet for instance collected the Facebook posts written by one of our candidate author, for which more support could have been helpful (Zadrozny and Collins, 2018). More generally, other individuals not listed here could of course have participated in the writing. This paper is available on arxiv under CC BY 4.0 DEED license. Authors: (1) Florian Cafiero (ORCID 0000-0002-1951-6942), Sciences Po, Medialab; (2) Jean-Baptiste Camps (ORCID 0000-0003-0385-7037), Ecole nationale des chartes, Universite Paris, Sciences & Lettres. Authors: Authors: (1) Florian Cafiero (ORCID 0000-0002-1951-6942), Sciences Po, Medialab; (2) Jean-Baptiste Camps (ORCID 0000-0003-0385-7037), Ecole nationale des chartes, Universite Paris, Sciences & Lettres. Table of Links Abstract and Introduction Abstract and Introduction Why work on QAnon? Specificities and social impact Why work on QAnon? Specificities and social impact Who is Q? The theories put to test Who is Q? The theories put to test Authorship attribution Authorship attribution Results Results Discussion Discussion Corpus constitution Corpus constitution Quotes of authors outside of the corpus have been Quotes of authors outside of the corpus have been Definition of two subcorpus: dealing with generic difference and an imbalanced dataset Definition of two subcorpus: dealing with generic difference and an imbalanced dataset The genre of “Q drops”: a methodological challenge The genre of “Q drops”: a methodological challenge Detecting style changes: rolling stylometry Detecting style changes: rolling stylometry Ethical statement, Acknowledgements, and References Ethical statement, Acknowledgements, and References Discussion If the author of the Qdrops is among our candidates, the results here seem to demonstrate the major role of Ron W. in the writing of the Qdrops, at least since the switch to 8chan. The peak of Paul F. in one of the two analyses, for the period before 8chan, could very well be revealing of a real participation, even a leading role for all the period before 8chan, with perhaps afterwards a brief period of collaboration (or competition), in between the migration to 8chan and what Paul F. himself describes as the last authentic Qdrop. Localised peaks of Christina U. or Michael F. on the other hand, while they might very hypothetically be revealing of more occasional collaborations, should probably not be over-interpreted. Given the nature of the coefficients used by the model for them (fig. 2), they seem more likely to be caused by ‘topic similarities’ due to the news and topics dealt with in the Qdrops. This could result in the choice of a similar lexicon, and even in quotations or paraphrases. Confusions between Michael F. or Christina U. on one hand, and other candidates (in training) or Q, seem due to attractions in terms of language register and generic peculiarities: samples that use a more elevated and grandiloquent type of patriotic address are brought somewhat closer to Michael F. samples, while those including heavier news-related content might be drawn towards Christina U. A more exploratory visualization of the feature space, based on correspondence analysis, shows a strong opposition (first dimension) between PaulF private posts and public writings, while the second one oppose PaulF and RonW samples (fig. 3). Projected in this feature space, the Qdrops from the 8Chan period, after the ‘board compromised’ post, mostly appear inside RonW data cloud, while previous posts, especially from the time of 4Chan are situated substantially closer to PaulF writings. This paper of course has limitations. The very nature of the Q drops, a genre in itself, and the brevity of these texts, make difficult to render a finer grain picture than the one we present here. It is thus plausible, even if we cannot demonstrate it, that other punctual interventions could have occurred. The training data we collected is important, and sufficient to obtain excellent performances. Yet, more training data could of course help increase the precision and reliability of the analyses. A media outlet for instance collected the Facebook posts written by one of our candidate author, for which more support could have been helpful (Zadrozny and Collins, 2018). More generally, other individuals not listed here could of course have participated in the writing. This paper is available on arxiv under CC BY 4.0 DEED license. This paper is available on arxiv under CC BY 4.0 DEED license. available on arxiv

Part of HackerNoon's growing list of open-source research papers, promoting free access to academic material.

Challenges in Building a QAnon Authorship Corpus

Machine Learning and Linguistic Profiles Sheds Light on Q's Possible Authors

Homepage: Ethnology.tech

Too Long; Didn't Read

Get Free Unlimited IP Geo Data With IPinfo Lite

Linguistic Analysis Reveals Authorship Changes and Collaboration in QDrop Posts

About Author

Comments

TOPICS

THIS ARTICLE WAS FEATURED IN

Related Stories

A Roadmap for Addressing Critical Challenges in Human-Machine Social Systems

Social Impacts, Global Reach, and the Mystery of 'Q'

Challenges in Building a QAnon Authorship Corpus

QAnon Text Analysis shows Imbalanced Datasets and Generic differences

Analyzing QDrops Texts shows Distinct Linguistic Features than Traditional Ones

Rolling Stylometry and Machine Learning Analyzes QAnon Texts Patterns

A Roadmap for Addressing Critical Challenges in Human-Machine Social Systems

Social Impacts, Global Reach, and the Mystery of 'Q'

Challenges in Building a QAnon Authorship Corpus

QAnon Text Analysis shows Imbalanced Datasets and Generic differences

Analyzing QDrops Texts shows Distinct Linguistic Features than Traditional Ones

Rolling Stylometry and Machine Learning Analyzes QAnon Texts Patterns

Light-Mode

Classic

Newspaper

Dark-Mode

Neon Noir

Minty

HN StartUps