Automatically profiling the author of an anonymous text

Abstract

Imagine that you have been given an important text of unknown authorship, and wish to know as much as possible about the unknown author (demographics, personality, cultural background, etc.), just by analyzing the given text. This authorship profiling problem is of growing importance in the current global information environmentapplications abound in forensics, security, and commercial settings. For example, authorship profiling can help police identify characteristics of the perpetrator of a crime when there are too few (or too many) specific suspects to consider. Similarly, large corporations may be interested in knowing what types of people like or dislike their products, based on analysis of blogs and online product reviews. The question we therefore ask is: How much can we discern about the author of a text simply by analyzing the text itself? It turns out that, with varying degrees of accuracy, we can say a great deal indeed. Unlike the problem of authorship attribution (determining the author of a text from a given candidate set), discussed recently in these pages by Li, Zheng, and Chen (2006), authorship profiling does not begin with a set of writing samples from known candidate authors. Instead, we exploit the sociolinguistic observation that different groups of people speaking or writing in a particular genre and in a particular language use that language differently (Chambers et al. 2004). That is, they vary in how often they use certain words or syntactic constructions (in addition to variation in, e.g., pronunciation or intonation). The particular profile dimensions we consider here are author gender (Argamon et al. 2003), age (Koppel et al. 2006), native language

Computational methods in authorship attribution

“…More recently, Graham et al (2005) and Zheng et al (2006) used neural networks on a wide variety of features. Other studies used k-nearest neighbor (Kjell et al 1995;Hoorn et al 1999;Zhao & Zobel 2005), Naive Bayes (Kjell 1994a;Hoorn et al 1999;Peng et al 2004), rule learners (Holmes & Forsyth 1995;Holmes 1998;Argamon et al 1998;Koppel & Schler 2003;Abbasi & Chen 2005;Zheng et al 2006), support vector machines (De Vel et al 2001;Diederich et al 2003;Koppel & Schler 2003, Abbasi & Chen 2005Koppel et al 2005;Zheng et al 2006), Winnow (Koppel et al 2002;Argamon et al 2003;Koppel et al 2006a), and Bayesian regression Madigan et al 2006;Argamon et al 2008). Further details regarding these studies can be found in the Appendix.…”

“…This bottoms out at the leaves, which are labeled by sets of individual words. These taxonomies can be used to construct features for stylistic text classification as has been done for authorship attribution on texts in English (Whitelaw et al 2004;Argamon et al 2007Argamon et al , 2008 and Portuguese (Pavelec 2007).…”

“…For example, one author may prefer to use the words start and large, where another may prefer begin and big (Mosteller andWallace 1964, Koppel et al 2006a). Such patterns of lexical choice can be represented by modeling the relative frequencies of content words (Martindale and McKenzie 1995;Craig 1999;Waugh et al 2000;Diederich et al 2003;Hoover 2004aHoover , 2004bArgamon et al 2008). Typically very rare words and those with near-uniform distribution over the corpus of interest can be omitted (Forman 2003), so that a reasonable set of perhaps several thousand words may used.…”

“…We will see in Section 6 that essentially the same methods that we used above for distinguishing individual authors can be used to distinguish between classes of authors, such as males and females or writers of different ages. The discussion in Section 6 is drawn from that in Argamon et al (2008).…”

“…As in Argamon et al (2008), we consider the following profile dimensions: author gender (Koppel et al 2002;Argamon et al 2003), age (Burger and Henderson 2006;Schler et al 2006), native language (Koppel et al 2005) and neuroticism level (Pennebaker & King 1999;Pennebaker, Mehl, & Niederhoffer, 2003). For each of these, we assemble an appropriately labeled corpus and proceed exactly as described above.…”