语言与文风201233bits.org编辑精选
写作风格足以识别网络匿名作者吗？
Is Writing Style Sufficient to Deanonymize Material Posted Online?
导读
这篇是计算语言学研究者介绍“文体计量学”（stylometry）研究的博客：仅凭写作风格（虚词频率、标点、句长等），能否把网上匿名发表的文字和真实作者对上号。结论是跨语境识别并不容易，只有约一成作者能以较高精度被认出来，但风格确实是一种稳定的“指纹”。对写作者来说，它提醒我们：你的用词与句式习惯比你以为的更暴露，也更稳定。
可以直接用的写作要点
- 01
风格指纹主要藏在你不自觉的习惯里——虚词频率、标点用法、平均句长，而非题材内容，改题材并不能改掉风格。
- 02
同一作者在不同语境下的文字仍可被统计比对，因此刻意“换身份写作”需要主动打破自己的句式与用词惯性。
- 03
高精度不等于可靠识别：在几十上百万人的池子里比对时，误报率会吞掉准确率，评估任何“识别”结论都要先看基数。
- 04
想让自己的风格可辨识，就要在细节上保持一致性；想隐藏风格，就要在细节上刻意制造差异，两者都靠对自身语言习惯的觉察。
HN 评论精选
真实案例：措辞习惯的相似性足以让亲近的人识别作者，风格比对自古就有效。
这让人想到“大学炸弹客”泰德·卡钦斯基被捕的方式：1995年9月宣言发表一周后，他的弟弟大卫在妻子琳达的鼓励下读了宣言，逐渐认真对待自己的怀疑，翻查家中旧文件，找到泰德上世纪70年代寄给报社、抗议技术滥用的信件，其中的措辞与宣言高度相似。
simonsarris查看英文原文
Reading this it came to mind and is perhaps worth mentioning that this is how the Unabomber (Ted Kaczynski), the Luddite who engaged in a mail bombing campaign that spanned nearly 20 years, was caught. Before the publication of the manifesto, Theodore Kaczynski's brother, David Kaczynski, was encouraged by his wife Linda to follow up on suspicions that Ted was the Unabomber. David Kaczynski was at first dismissive, but progressively began to take the likelihood more seriously after reading the manifesto a week after it was published in September 1995. David Kaczynski browsed through old family papers and found letters dating back to the 1970s written by Ted and sent to newspapers protesting the abuses of technology and which contained phrasing similar to what was found in the Unabomber Manifesto http://en.wikipedia.org/wiki/Ted_Kaczynski#Search
批评精度数字被高估：大池子比对下误报率极高，而真正需要保护的跨话题匿名恰恰是工具最弱的地方。
这些结果对隐私的威胁其实没那么大。比如80%的精度，放到上千万人的池子里就没用了——就像一种千分之一发病率的病，检测准确率99%，阳性里仍有九成是误报。类似的还有“能以约65%准确率判断作者性别”的说法，那几乎不比瞎猜好多少。作者提到可以用话题辅助关联，但如果我在自己的专业领域写匿名帖，根本不用文体分析就能猜到我是谁；这方面的隐私从来就不存在。真正需要隐私的恰恰是谈论无关话题的时候：一个人应该能以多个化名表达不同面向，程序员业余写小说，没必要让雇主知道。
DarkShikari查看英文原文
While impressive, I don't think these results are actually that bad for privacy. 80% precision, for example, is useless when you're matching against tens of millions. It's much the same fallacy of the medical test for a disease that occurs in 1 out of 1000 people, and which has 99% accuracy -- but that still means a 90% false positive rate. It reminds me of the claims of being able to identify, for example, the gender of an author with ~65% accuracy -- which is really actually completely unimpressive, as it's hardly better than guessing, and certainly not something you could rely on for any serious purpose. The author mentions that topic is one way to help correlate beyond the results of the algorithm. But if I wrote "anonymous" posts in my area of expertise, you certainly would not need stylistic analysis to guess what my identity might be! There has never been privacy in this regard, I don't think. Where privacy is needed most, I think, is exactly where this deanonymizing tool still isn't sufficient: talking about unrelated topics. A person should be free to express themselves under multiple names for different purposes, and there is no reason why an employer needs to know about a programmer's side hobby as a fiction writer if s/he doesn't want them to. Finally, I do wonder how well these results correlate to the case where someone is intentionally operating under a different name. Matching one post by tech blogger A against blogger A is easy, because tech blogger A is making no attempt to write any differently or in any different context. However, what if tech-writer A ghost-wrote YA fiction on the side? Could you use these techniques to detect that the fiction was written by that blogger? It can't be ruled out without trying, but generalizing these results to that seems questionable.
关注“哪些具体风格特征最容易暴露作者”，提示写作者注意少数异常用词习惯的辨识度。
跨语境识别这么难，反而让我有点意外。我原以为只要找到合适的数据集（这是此前研究的主要障碍），准确率会相当高，就像凭几十个特征猜浏览器指纹那样容易。但结果显示，只有约10%的作者能被以80%的精度识别出来——即便不刻意掩饰，多数人也暂时不会被自动识别（不过原文也说得对：如果锁定特定目标，人为补充一些特征大概能提高命中率）。我感兴趣的是：是否存在某些特征让跨语境识别特别容易，类似“十大暴露特征”的清单？那10%能被高精度识别的人，是因为集中了少数不寻常的语法或词汇特征，还是特征分布比较弥散？
_delirium查看英文原文
The difficulty of doing it cross-context is actually slightly more surprising to me than the possibility. I would've guessed that, once a suitable data set were found (a main impediment to previous studies), accuracy would be quite good, along the lines of how easy it is to guess browser fingerprints from a few dozen telltale markers. But it appears that only about 10% of authors can be guessed to a precision of 80%, which is still pretty decent odds of not being identified automatically, at least for now, even without actively trying to cover up (though the linked post is right that with a specific target, intelligently adding some ad-hoc additional features can probably help). One thing that'd be interesting to me is whether there are certain characteristics that make it particularly easy to identify people cross-context, like a top-10-telltale-markers sort of thing. Are a disproportionate number of the 10% who can be identified with high precision using a handful of unusual grammatical or lexical features, or is it more of a diffuse sort of thing?
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