Detecting Authorship in Political Texts with Inductive Stylometry

• URL: http://arxiv.org/abs/2609.08459v1
• Date: Tue, 08 Sep 2026 08:57:17 GMT
• Title: Detecting Authorship in Political Texts with Inductive Stylometry
• Abstract summary: This paper develops and stress-tests an inductive stylometric approach for recovering latent authorship structure in political communication.<n>We apply the approach to six corpora that vary in length (from tweets to long documents), in mode (written and oral), and in language (English and Hungarian)<n>It sorts a politician's tweets into validated subsets while uncovering additional insights, and distinguishes scripted from improvised speech.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Political texts are rarely authored by the nominal speaker alone. Tweets, speeches, reports, and official statements are drafted, edited, or harmonized by staff, yet political science has paid limited attention to the stylistic traces these hidden authors leave behind. This paper develops and stress-tests an inductive stylometric approach for recovering latent authorship structure in political communication, combining character 3-gram features with UMAP dimensionality reduction, and Burrows' Delta. We apply the approach to six corpora that vary in length (from tweets to long documents), in mode (written and oral), and in language (English and Hungarian). The approach recovers near-disjoint analyst fingerprints in formal legal prose in both languages, sorts a politician's tweets into validated subsets while uncovering additional insights, and distinguishes scripted from improvised speech. It fails, however, to resolve individual speechwriters within scripted corpora. Frequency-based stylometry is thus a powerful tool that, depending on authorial signal strength and institutional editing, can uncover authorship traces relevant to legislative studies, political communication, and policy research.

Related papers

• SpeechSense: A Paralinguistic-Focused Dataset for Fine-Grained Speech Sentiment Analysis [45.09009423124683]
  Speech Sentiment Analysis plays a critical role in decoding paralinguistic cues for diverse real-world applications.<n>Existing approaches rely on text-centric pipelines that cascade Automatic Speech Recognition with text analysis.<n>We propose a novel dataset, SpeechSense, for fine-grained speech sentiment analysis.
  arXiv  Detail & Related papers  (2026-08-18T15:51:27Z)
• Quantifying Speaker Embedding Phonological Rule Interactions in Accented Speech Synthesis [44.55147169458465]
  We analyze the interaction between speaker embeddings and linguistically motivated phonological rules in accented speech synthesis.<n>Experiments show that combining rules with embeddings yields more authentic accents.<n>Our findings highlight rules as a lever for accent control and a framework for evaluating disentanglement in speech generation.
  arXiv  Detail & Related papers  (2026-01-20T19:25:33Z)
• StegoStylo: Squelching Stylometric Scrutiny through Steganographic Stitching [0.0]
  Stylometry supports copyright and plagiarism investigations, aids detection of harmful content, and provides historical context for literary works.<n>Stylometry is employed as a tool for authorship verification--confirming whether a text truly originates from a claimed author--it can also be weaponized for malicious purposes.<n>This paper explores how adversarial stylometry combined with steganography can counteract stylometric analysis.
  arXiv  Detail & Related papers  (2026-01-14T00:49:20Z)
• A stylometric analysis of speaker attribution from speech transcripts [0.5371337604556311]
  Forensic scientists often need to identify an unknown speaker or writer in cases such as ransom calls, covert recordings, alleged suicide notes, or anonymous online communications.<n>Speaker recognition in the speech domain usually examines phonetic or acoustic properties of a voice.<n>If a speaker disguises their voice or employs text-to-speech software, vocal properties may no longer be reliable.
  arXiv  Detail & Related papers  (2025-12-15T18:55:25Z)
• Large Language Models and Forensic Linguistics: Navigating Opportunities and Threats in the Age of Generative AI [0.0]
  Large language models (LLMs) serve as powerful analytical tools enabling scalable corpus analysis and embedding-based authorship attribution.<n>Recent stylometric research indicates that LLMs can approximate surface stylistic features yet exhibit detectable differences from human writers.<n>The article concludes that forensic linguistics requires methodological reconfiguration to remain scientifically credible and legally admissible.
  arXiv  Detail & Related papers  (2025-12-07T17:05:31Z)
• Latent Topic Synthesis: Leveraging LLMs for Electoral Ad Analysis [51.95395936342771]
  We introduce an end-to-end framework for automatically generating an interpretable topic taxonomy from an unlabeled corpus.<n>We apply this framework to a large corpus of Meta political ads from the month ahead of the 2024 U.S. Presidential election.<n>Our approach uncovers latent discourse structures, synthesizes semantically rich topic labels, and annotates topics with moral framing dimensions.
  arXiv  Detail & Related papers  (2025-10-16T20:30:20Z)
• ChatGPT for President! Presupposed content in politicians versus GPT-generated texts [0.0]
  This study examines ChatGPT-4's capability to replicate linguistic strategies used in political discourse.<n>Using a corpus-based pragmatic analysis, this study assesses how well ChatGPT can mimic these persuasive strategies.
  arXiv  Detail & Related papers  (2025-03-03T07:48:04Z)
• Keep It Private: Unsupervised Privatization of Online Text [13.381890596224867]
  We introduce an automatic text privatization framework that fine-tunes a large language model via reinforcement learning to produce rewrites that balance soundness, sense, and privacy. We evaluate it extensively on a large-scale test set of English Reddit posts by 68k authors composed of short-medium length texts.
  arXiv  Detail & Related papers  (2024-05-16T17:12:18Z)
• EXPRESSO: A Benchmark and Analysis of Discrete Expressive Speech Resynthesis [49.04496602282718]
  We introduce Expresso, a high-quality expressive speech dataset for textless speech synthesis. This dataset includes both read speech and improvised dialogues rendered in 26 spontaneous expressive styles. We evaluate resynthesis quality with automatic metrics for different self-supervised discrete encoders.
  arXiv  Detail & Related papers  (2023-08-10T17:41:19Z)
• A Corpus for Sentence-level Subjectivity Detection on English News Articles [49.49218203204942]
  We use our guidelines to collect NewsSD-ENG, a corpus of 638 objective and 411 subjective sentences extracted from English news articles on controversial topics. Our corpus paves the way for subjectivity detection in English without relying on language-specific tools, such as lexicons or machine translation.
  arXiv  Detail & Related papers  (2023-05-29T11:54:50Z)
• PART: Pre-trained Authorship Representation Transformer [52.623051272843426]
  Authors writing documents imprint identifying information within their texts.<n>Previous works use hand-crafted features or classification tasks to train their authorship models.<n>We propose a contrastively trained model fit to learn textbfauthorship embeddings instead of semantics.
  arXiv  Detail & Related papers  (2022-09-30T11:08:39Z)
• A Novel Corpus of Discourse Structure in Humans and Computers [55.74664144248097]
  We present a novel corpus of 445 human- and computer-generated documents, comprising about 27,000 clauses. The corpus covers both formal and informal discourse, and contains documents generated using fine-tuned GPT-2.
  arXiv  Detail & Related papers  (2021-11-10T20:56:08Z)
• Sentiment analysis in tweets: an assessment study from classical to modern text representation models [59.107260266206445]
  Short texts published on Twitter have earned significant attention as a rich source of information. Their inherent characteristics, such as the informal, and noisy linguistic style, remain challenging to many natural language processing (NLP) tasks. This study fulfils an assessment of existing language models in distinguishing the sentiment expressed in tweets by using a rich collection of 22 datasets.
  arXiv  Detail & Related papers  (2021-05-29T21:05:28Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.