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"The algorithm will screw you": Blame, social actors and the 2020 A Level results algorithm on Twitter
Dan Heaton1, Elena Nichele1,2, Jeremie Clos1
1School of Computer Science, University of Nottingham, Nottingham, Nottinghamshire, United Kingdom.
Plos One
|July 26, 2023
Summary
The 2020 A Level algorithm scandal saw blame attributed to the algorithm, UK government, and Ofqual. Corpus Linguistics and Critical Discourse Analysis revealed nuanced social actor representation, with students assigned limited blame.
Area of Science:
- Computational Linguistics
- Social Sciences
- Discourse Analysis
Background:
- In August 2020, the UK replaced A Level exams with an algorithm, later switching to teacher grades after public outcry.
- Previous analysis of Twitter discourse used NLP-based opinion mining but faced accuracy and interpretation limitations.
- The 2020 A Level grading scandal generated significant public discourse on social media regarding accountability.
Purpose of the Study:
- To analyze blame attribution in Twitter discourse concerning the 2020 A Level algorithm scandal.
- To complement existing NLP research by applying Corpus Linguistics (CL) and Critical Discourse Analysis (CDA).
- To examine social actor representation and agency in the discourse surrounding the algorithm.
Main Methods:
- Analysis of 18,239 tweets related to the 2020 A Level algorithm.
- Application of Corpus Linguistics (CL) and Critical Discourse Analysis (CDA) methodologies.
- Examination of transitivity and social actor representation to identify blame attribution.
Main Results:
- The algorithm, UK government, and Ofqual were implicated as social actors responsible for the A Level grading issues.
- Blame was attributed through active agency, metaphorical possession, and passive constructions.
- Students were found to have limited blame assigned to them within the analyzed discourse.
Conclusions:
- CL and CDA offer valuable complementary approaches to NLP tools for analyzing complex social discourse like the A Level scandal.
- The varied linguistic constructions used in the discourse served to obscure clear blame attribution.
- Further research can explore iterative applications of CL and CDA for deeper insights into public discourse.
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