# A White-Box Sociolinguistic Model for Gender Detection
## 2 Automatic Gender Detection: From Discriminant Analysis to Deep Learning
### First Stage  
Early automatic gender detection studies examined formal texts, such as the British National Corpus or the Enron e-mail corpus, or datasets collected from controlled settings. Research on automatic gender detection was constrained due to the unavailability of annotated data.  
In [16], [PERSON] and [PERSON] conducted three experiments to analyze gender-preferential language style in electronic discourse. They computed the number of messages, the total word count, the sentence length as well as other lexical and morphological features, such as references to emotion, compliments, insults, apologies, intensive adverbs, subordinating conjunctions, and adjectives, among others. Although they detected few significant differences in the electronic messages written by the 35 participants, they correctly classified 91.4% of the authors using a discriminant analysis.  
[PERSON] [17] examined gender differences related to lexical richness in free and spontaneous recorded conversations produced by 30 participants. Specifically, he applied eight lexical richness measures, such as noun-rate and adjective-rate per 100 words, and type-token ratio. By performing a statistical discriminant analysis, [PERSON] reached a classification accuracy of 90%.  
In 2002, [PERSON] et al. [18] analyzed an e-mail corpus sourced from a large academic organization. Initially, they collected 8820 e-mail documents written by 342 authors. They reported a 70.2% F\({}_{1}\) score with a Support Vector Machine classifier and 221 features, organized into seven categories: document-based, word-based, character-based, function words, structural, gender-preferential, and other features. The so-called gender-preferential features mainly consisted of the frequency of adjectives and adverbs, along with the frequency of the word _sorry_ and apology-related words.  
[PERSON], [PERSON] and [PERSON] [19] conducted a gender detection study on 566 documents from the British National Corpus labeled both for author gender and for genre (fiction and non-fiction). They proposed a learning method based on the Exponential Gradient algorithm to find a linear separator between female-authored and male-authored texts. They used 405 function words and the 500 most common ordered triples, the 100 most common ordered pairs and all the single parts-of-speech tags as features. With this method, they reached approximately 80% accuracy inferring the authors' gender and 98% on genre identification.  
In 2005, [PERSON] and [PERSON] [20] performed a computational analysis on the [PERSON] corpus made up of 12,000 recorded telephone conversations. They extracted word unigrams and bigrams as features, and they tested various Machine-Learning algorithms, such as Naive Bayes, Maximum Entropy, and Rocchio. The maximum accuracy of 92.5% was achieved by a Support Vector Machine classifier using about 300 K word bigrams.  
The consolidation of the blogosphere with the expansion of platforms, including blogger.com or wordpress.org, represented a paradigm shift in automatic gender detection.