使用序数模式和信息论测度对期刊专栏进行结构分析

中文摘要 AI 辅助

新闻文本中的来源归属通常通过语义表示来实现，如词加权或神经嵌入。这些方法能捕捉主题内容，但通常忽略文本作为序列的组织方式。本文表明，纯粹的结构、与内容无关的特征本身就能区分新闻来源。我们将秘鲁14家在线报纸的文章通过词长和词汇频率两种独立编码转换为数字序列，然后通过序数模式分析进行分析，计算模式和转移概率、排列熵、不平衡和统计复杂性。两种编码都产生了非均匀的序数模式分布、明确的优先转移以及跨来源的连贯熵复杂性特征。无监督聚类在特征空间中恢复了三个连贯组，基于这些特征训练的监督分类器准确率高达0.99，即使在大量特征约简下仍保持稳定。这些结果在两种结构不相关的编码中的一致性强烈表明，序数特征捕捉到了每个来源写作风格的真实动态指纹，而非任何一种表示的假象，这将该框架扩展到了西班牙语新闻领域，该领域在基于复杂性的文本分析中相对未得到充分探索。

英文摘要

Source attribution in journalistic text is typically approached through semantic representations, such as term weighting or neural embeddings. These approaches capture topical content, but usually overlook how a text is organized as a sequence. Here, we show that purely structural, content-independent features can discriminate between news sources on their own. We converted articles from fourteen Peruvian online newspapers into numerical sequences using two independent encodings, word length and lexical frequency. Then, we analyzed each of them through ordinal pattern analysis, computing pattern and transition probabilities, permutation entropy, disequilibrium, and statistical complexity. Both encodings yield non-uniform ordinal pattern distributions, well-defined preferential transitions, and coherent entropy complexity signatures across sources. Unsupervised clustering recovers three coherent groups in the feature space, and supervised classifiers trained on these features achieve accuracy up to 0.99, which remains stable even under substantial feature reduction. The consistency of these results across two structurally unrelated encodings strongly indicates that ordinal features capture a genuine dynamical fingerprint of each source's writing style rather than an artifact of either representation, which extends this framework to Spanish-language journalism, a setting that remains comparatively underexplored in complexity-based text analysis.

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15 pages, 5 figures