Asymmetric Discourse Homogenization and Shared Language Technology: Evidence from Reddit

Fengming Liu

cs.CYAug 13, 2026

Asymmetric Discourse Homogenization and Shared Language Technology: Evidence from Reddit

Authors: Fengming Liu

Abstract

I document an ideologically asymmetric break in the pre-existing diversification trend of political discourse, emerging around late 2022, using 6 million Reddit comments from two cross-partisan forums, 2019-2025. Conservative users experienced an interruption of their prior diversification trajectory; progressive users showed no comparable change. The asymmetry is consistent across estimation strategies (ITS, DiD, RDiT, propensity-score matching) and temporal aggregations. A daily-frequency permutation test over 2,377 candidate cutoff dates shows the ChatGPT threshold produces an unremarkable estimate (49.8th percentile): the shift builds gradually instead of breaking at a single date. A continuous cumulative LLM index, tracking AI exposure across seven model releases, remains significant under a quadratic trend specification that eliminates the binary estimate. A stayer analysis narrows the mechanism: the homogenization effect disappears when the sample is restricted to authors active throughout the study period, and the stayer confidence interval excludes within-author effects even a tenth the size of the full-sample estimate. The mechanism is most parsimoniously ecological (community-level discursive convergence) rather than individual-level AI adoption, though the data cannot cleanly separate this account from concurrent secular change.

Figures & tables

Figure 1: Monthly within-group semantic similarity, 2019–2025. The vertical dashed line marks the ChatGPT launch (November 2022); the dotted line marks the GPT-4 release (March 2023) and the dash-dotted line the Dobbs decision (June 2022), neither of which produces a large permutation-test shift (Section 4.2). Right-wing users in r/AskConservatives show the most pronounced departure from pre-treatment trends.

	AskCons./Right	AskCons./Left	AskLib./Right	AskLib./Left
Level shift ( $\gamma$ )	$+0.0081^{***}$	$+0.0012$	$+0.0044^{**}$	$-0.0000$
	$(0.000)$	$(0.420)$	$(0.002)$	$(0.980)$
Slope change ( $\delta$ )	$+0.0004^{***}$	$+0.0002^{**}$	$-0.0001^{*}$	$-0.0001$
	$(0.000)$	$(0.002)$	$(0.017)$	$(0.181)$
Pre-trend ( $\beta$ )	$-0.0005^{***}$	$-0.0001^{*}$	$-0.0001^{***}$	$-0.0000$
	$(0.000)$	$(0.045)$	$(0.000)$	$(0.276)$

Table 1: Interrupted Time Series Estimates by Subreddit and Ideology

Figure 2: ITS fit for Right-wing users in r/AskConservatives.

Figure 3: Out-of-sample prediction: (a) actual within-group similarity against the linear trend extrapolated from pre-ChatGPT data (2019-01 to 2022-10), and (b) the prediction gap (actual minus predicted) over time. The right-wing gap (dark red) is positive and growing, while the left-wing gap (light orange) is substantially smaller.

Aggregation	Group	$\tau$	NW-HAC SE	$p$
Monthly	AskCons./Right	$+0.0080$	$0.0018$	$<0.001^{***}$
Monthly	AskCons./Left	$+0.0012$	$0.0017$	$0.491$
Monthly	AskLib./Right	$+0.0044$	$0.0012$	$<0.001^{***}$
Monthly	AskLib./Left	$-0.0000$	$0.0009$	$0.982$
Weekly	Right	$+0.0028$	$0.0006$	$<0.001^{***}$
Weekly	Left	$-0.0004$	$0.0006$	$0.469$

Table 2: RDiT Local-Linear Estimates by Temporal Aggregation

Panel regressions on matched daily data (Right $\times$ Post, HC1)
Specification	$N$ pairs	DID	Paired $t$	$p$	Cohen’s $d$
(1) Writing style (11 vars)	21	$-0.0026$	$-1.317$	$0.203$	$-0.42$
(2) Style + discourse (14 vars)	19	$-0.0062$	$-3.349$	$0.004$	$-1.11$
(3) Full (16 vars)	16	$-0.0065$	$-2.669$	$0.018$	$-1.02$
Panel (1)	$-0.0013$		$p=0.004$		$N=30{,}275$
Panel (2)	$-0.0030$		$p<0.001$		$N=26{,}003$

Table 3: Matching Estimates of Right–Left Differential Post-ChatGPT Similarity Change

Figure 4: ITS level shift (

\gamma

) in within-flair similarity for all right-wing flairs, with 95% CI. Social Conservative and Nationalist show the largest positive shifts; Conservative and Constitutionalist shift negatively.

	(1)	(2)	(3)	(4)
	ITS	cum_llm + t	cum_llm + t + t 2	log_adopt + t
cum_llm ( $\beta$ )		$+0.0039^{***}$	$+0.0026^{**}$
		$(0.000)$	$(0.003)$
log_adopt ( $\beta$ )				$+0.017^{***}$
				$(0.000)$
post ( $\gamma$ )	$+0.0081^{***}$

Table 4: Cumulative LLM Index Regressions: Within-Group Similarity (AskConservatives/Right)

	Covariate alone ( $t$ + $t^{2}$ )			Nested with cum_llm
Covariate	$\beta$	NW $p$	$R^{2}$	cum_llm $p$	cov. $p$	$R^{2}$
Trump mention share	$+0.012$	$0.537$	$0.658$	$0.002^{**}$	$0.676$	$0.687$
Political keyword share	$+0.021$	$0.152$	$0.671$	$0.037^{*}$	$0.535$	$0.688$
Biden mention share	$+0.014$	$0.828$	$0.656$	$0.002^{**}$	$0.639$	$0.688$
Politicization gap (R–L)	$+0.039^{*}$	$0.021$	$0.697$	$0.002^{**}$	$0.026^{*}$	$0.724$
Right-wing volume share	$+0.025^{*}$	$0.044$	$0.699$	$<0.001^{***}$	$0.013^{*}$	$0.749$

Table 5: Rival Continuous Covariates vs. the Cumulative LLM Index (AskConservatives/Right)

Group	Sample	$\gamma$ (level shift)	95% CI	$p$	$R^{2}$
AskCons./Right	Stayer (DeBERTa)	$-0.0001$	$[-0.00016,+0.00003]$	$0.194$	$0.604$
AskCons./Right	Full (DeBERTa)	$+0.0081$	$[+0.0053,+0.0108]$	$<0.001^{***}$	$0.694$
AskCons./Left	Stayer (DeBERTa)	$+0.0000$	$[-0.00014,+0.00016]$	$0.903$	$0.037$
AskCons./Left	Full (DeBERTa)	$+0.0012$	$[-0.0017,+0.0041]$	$0.420$	$0.131$
AskLib./Right	Stayer (DeBERTa)	$+0.0001$	$[-0.00003,+0.00019]$	$0.152$	$0.329$
AskLib./Right	Full (DeBERTa)	$+0.0043$	$[+0.0016,+0.0071]$	$0.002^{**}$	$0.435$

Table 6: Stayer Analysis: ITS on Within-Group Similarity, Stayer-Only Sample

Figure 5: Information entropy evidence (full corpus,

N=6{,}056{,}593

). (a) Within-month standard deviation of word entropy: right-wing shows a sharp decline (

d=-2.49

); left-wing decline is smaller (

d=-1.57

). The annotated reference line marks the ChatGPT word-level entropy SD from the HC3 benchmark (

0.408

); the character-level SD used in the text comparison is

0.076

, a different entropy metric. (b) Absolute left–right gap in character-entropy SD narrows post-ChatGPT (

p=0.050

Appendix figures & tables3 assets

Supplementary material from the paper’s appendix.

Appendix

Group	Pre	Post	$\Delta$	$p$	Cohen’s $d$
Within-group similarity
AskALiberal / Left	0.9253	0.9232	$-0.0022$	$<0.001$	$-0.841$
AskALiberal / Right	0.9249	0.9223	$-0.0027$	$<0.001$	$-0.755$
AskConservatives / Left	0.9236	0.9234	$-0.0002$	$0.794$	$-0.060$
AskConservatives / Right	0.9289	0.9233	$-0.0056$	$<0.001$	$-0.889$
Cross-ideology similarity (Left vs. Right within subreddit)

Appendix

Table A1 : Pre/Post Comparison of Semantic Similarity by Group

	AskConservatives	AskALiberal
Right	$+0.0053^{***}$	$-0.0004$
	$(0.000)$	$(0.509)$
Post	$+0.0087^{***}$	$+0.0020^{*}$
	$(0.000)$	$(0.012)$
Right $\times$ Post	$-0.0053^{***}$	$-0.0005$
	$(0.000)$	$(0.549)$

Appendix

Table A2 : Difference-in-Differences Estimates

Group-specific spec: $t+\text{Right}\times t$ slopes, HC1 SE. NW-HAC (6 lags)
	AskConservatives		AskALiberal
Specification	Right $\times$ Post	$p$	Right $\times$ Post	$p$
Common trend (Table A2 )	$-0.0053$	$<0.001$	$-0.0005$	$0.549$
Group-specific pre-trends	$+0.0077$	$0.005$	$+0.0040$	$0.012$
gives $p=0.010$ (AskCons.) and $p=0.005$ (AskALib.) for the interaction.
Pre-period slope gap (Right minus Left): $-0.00038$ per month, $p<0.001$ (AskCons.);

Appendix

Table A3 : DiD Reconciliation: The Sign Contradiction Is a Parallel-Trends Specification Effect

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How Far Did They Go? The Persuasive Tactics of Covert LLM Agents in a Discontinued Field Experiment

Kokil Jaidka, Saifuddin Ahmed

Department of Communications and New Media, & Centre for Trusted Internet & Community, National University of Singapore, Singapore · Wee Kim Wee school of Communication and Information, Nanyang Technological University, Singapore

LLM Auditing LLM Agent Evaluation

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