EvoTaxo: Building and Evolving Taxonomy from Social Media Streams

Yiyang Li; Tianyi Ma; Yanfang Ye

cs.CLMar 20, 2026

EvoTaxo: Building and Evolving Taxonomy from Social Media Streams

Authors: Yiyang Li, Tianyi Ma, Yanfang Ye

Organizations: University of Notre Dame

Abstract

Constructing taxonomies from social media corpora is challenging because posts are short, noisy, semantically entangled, and temporally dynamic. Existing taxonomy induction methods are largely designed for static corpora and often struggle to balance robustness, scalability, and sensitivity to evolving discourse. We propose EvoTaxo, a LLM-based framework for building and evolving taxonomies from temporally ordered social media streams. Rather than clustering raw posts directly, EvoTaxo converts each post into a structured draft action over the current taxonomy, accumulates structural evidence over time windows, and consolidates candidate edits through dual-view clustering that combines semantic similarity with temporal locality. A refinement-and-arbitration procedure then selects reliable edits before execution, while each node maintains a concept memory bank to preserve semantic boundaries over time. Experiments on two Reddit corpora show that EvoTaxo produces more balanced taxonomies than baselines, with clearer post-to-leaf assignment, better corpus coverage at comparable taxonomy size, and stronger structural quality. A case study on the Reddit community /r/ICE_Raids further shows that EvoTaxo captures meaningful temporal shifts in discourse. Our codebase is available here.

Figures & tables

Figure 1: K-Means clustering of raw social media posts and their corresponding LLM-generated actions, where the number of clusters is selected by the highest silhouette score. Compared with raw posts, action representations form substantially clearer cluster structure, motivating action-level consolidation in EvoTaxo.

Figure 2: Overview of EvoTaxo. The framework starts from an LLM-generated seed taxonomy with concept memory banks and processes posts chronologically. Each post is mapped to a draft action. Structural actions are accumulated and consolidated at the window boundary under semantic and semantic-temporal views, then refined and arbitrated into final actions. These final actions update both the taxonomy and its temporal grounding records.

Dataset	Method	Nodes	Leaf	Ent $\downarrow$	Uncls $\downarrow$	NLIV-S $\uparrow$	Path $\uparrow$	Sib-C $\uparrow$	Sib-S $\uparrow$	Tokens
/r/Opiates (2015-2024)	KN	7	7	0.93	0.85	0.40	3.16	4.00	3.00	<1
	Chain-of-Layer	40	40	0.83	0.16	0.58	4.13	4.00	3.00	<1
	TnT-LLM	25	25	0.90	0.29	0.36	3.04	4.00	3.00	60.9
	TaxoAdapt	73	62	0.95	0.30	0.52	3.38	4.27	3.36	164.4
	EvoTaxo (Ours)	25	19	0.79	0.22	0.83	4.68	4.60	3.40	35.3
/r/Opiates (2022-2024)	KN	6	6	0.93	0.86	0.46	2.83	3.00	4.00	<1

Table 1: Main results across datasets. EvoTaxo achieves the best trade-off between assignment quality and structural quality while maintaining a relatively balanced taxonomy. Tokens are reported in millions (M).

Method	Nodes	Leaf	Ent $\downarrow$	Uncls $\downarrow$	NLIV-S $\uparrow$	Path $\uparrow$	Sib-C $\uparrow$	Sib-S $\uparrow$
EvoTaxo	25	19	0.79	0.22	0.83	4.68	4.60	3.40
- w/o CMB	14	8	0.82	0.28	0.78	4.53	4.50	3.50
- w/o Time-Aware Clustering	10	8	0.80	0.50	0.80	4.50	4.00	4.00
- w/o Arbitration	70	54	0.92	0.17	0.74	4.00	3.67	3.00

Table 2: Ablation study on /r/opiates (2015–2024). Removing CMB weakens semantic stability, removing time-aware clustering harms coverage, and removing arbitration leads to reduced hierarchy consistency.

Figure 3: Monthly evolution of the taxonomy induced from /r/ICE_Raids , aggregated to the five top-level topics. The figure highlights three major turning points: an early concentration on personal-harm narratives, a mid-period protest-driven burst, and a later shift toward institutional oversight and policy discussion. The lower panel shows the number of newly introduced subtopics per month.

Figure 4: Taxonomy snapshot for /r/ICE_Raids after the June 2025 update. Green highlights mark subtopics newly added in June, illustrating how EvoTaxo converts a temporally concentrated protest-driven discourse burst into explicit taxonomy growth.

Appendix figures & tables2 assets

Supplementary material from the paper’s appendix.

Appendix

Metric	Sample Size	Exact Agr.	$\pm 1$ Agr.
Path	30	0.67	0.90
Sib-C	30	0.70	0.87
Sib-S	30	0.57	0.83

Appendix

Table 3: LLM-human agreement for the three LLM-judged structural metrics.

Figure 5: Yearly evolution of the taxonomy induced from /r/opiates , aggregated to the seven top-level topics. Early years are dominated by tapering/quitting and street-opiate quality and harm-reduction discussion. Later years show a stronger shift toward medical prescribing and treatment access.

Taxonomy Induction

See all 16 papers

TaxCE : A Framework for Automated Taxonomy Construction and Evaluation at Scale
Aug 31, 2026Sandeep Sricharan Mukku, Albert Aristotle Nanda, Rohit PyatiTopic Modeling Semantic Mapping
Amazon
Towards Automatic Evolution Tree Generation from Citation Graphs
Sep 9, 2026Zexing Zhao, Yuntong Hu, Liang ZhaoCitation Network Analysis Bibliometric Analysis
School of Mechanical Engineering, Georgia Institute of Technology · Department of Computer Science, Emory University
SPARROW: Scalable Taxonomy Induction via Structure-Preserving Partitioning and Constraint-Guided Merging
Sep 7, 2026Yirui Zhang, Yixuan Tang, Yandong Sun +2Spectral Clustering Taxonomy Induction
School of Computing, National University of Singapore

Aug 31, 2026J/K move · Enter open · S save

TaxCE : A Framework for Automated Taxonomy Construction and Evaluation at Scale

Sandeep Sricharan Mukku, Albert Aristotle Nanda, Rohit Pyati

Amazon

Topic Modeling Semantic Mapping Automated Evaluation Taxonomy Induction

Open paper AskPDF arXiv

Organizing unstructured feedback text into hierarchical taxonomy is a fundamental challenge in NLP, particularly in domains where feedback arrives at massive scale in varied forms such as reviews, transcripts, and surveys. Existing approaches either produce shallow hierarchies, neglect long-tail topics, or lack rigorous evaluation frameworks. We present TaxCE, a fully automated framework that constructs multi-level hierarchical taxonomies from raw text through progressive condensation of corpus content into actionable segments, deduplicated semantic units, and granular topics with definitions, which are then organized bottom-up into a hierarchy with corpus-groundedness. We also introduce three corpus-grounded evaluation metrics, Exclusivity, Exhaustivity, and Granularity (EEG), and integrate them into a metrics-in-the-loop iterative refinement mechanism that diagnoses deficiencies and applies targeted corrections until convergence. Extensive experiments demonstrate that TaxCE consistently outperforms existing baselines spanning classical topic models, neural methods, and LLM-based approaches, with average improvements of 11.8, 20.5, and 15.7 percentage points in exclusivity, exhaustivity, and granularity respectively over the strongest baseline. Human evaluation further confirms superior taxonomy quality, actionability, and navigability.

Abstract

Taxonomy Induction

Explore similar work

TaxCE : A Framework for Automated Taxonomy Construction and Evaluation at Scale

Towards Automatic Evolution Tree Generation from Citation Graphs

SPARROW: Scalable Taxonomy Induction via Structure-Preserving Partitioning and Constraint-Guided Merging