Ature: 65 to 79 classification accuracy. Of course, such a high/low model cannot directly be used for classifying unlabeled people as one would also need to know who fits in the middle. Regression is a more appropriate predictive task for continuous outcomes like age and personality, even though R scores are naturally smaller than binary classification accuracies. We ran an additional tests to evaluate only those words and phrases, topics, or LIWC categories that are selected via differential language analysis rather than all features. Thus, we used only those language features that significantly correlated (Bonferonnicorrected pv0:001) with the outcome being predicting. To keep consistent with the main evaluation, we used no controls, and so one could view this as a univariate feature selection over each type of feature independently. We again found significant improvement from using the open-vocabulary features over LIWC and no significant changes in accuracy overall. These results are presented in Table S2. In addition to demonstrating the greater informative value of open-vocabulary features, we found our results to be state-of-the-art. The highest previous out-of-sample accuracies for gender prediction based entirely on language were 88.0 over twitter data [68] while our classifiers reach an accuracy of 91.9 . Our increased performance could be attributed to our set of language features, a strong predictive algorithm (the support vector machine), and the large sample of Facebook data.DiscussionOnline social media such as Facebook are a particularly promising resource for the study of people, as “status” updates are self-descriptive, personal, and have emotional content [7]. Language use is objective and quantifiable behavioral data [96], and unlike surveys and questionnaires, Facebook language allows researchers to observe individuals as they freely present themselves in their own words. Differential language analysis (DLA) in social media is an unobtrusive and non-reactive window intothe social and psychological characteristics of people’s everyday concerns. Most studies linking language with psychological variables rely on a priori fixed sets of words, such as the LIWC categories carefully constructed over 20 years of human research [11]. Here, we show the benefits of an open-vocabulary approach in which the words analyzed are based on the data itself. We extracted words, phrases, and topics (automatically clustered sets of words) from millions of Facebook messages and found the language that correlates most with gender, age, and five factors of personality. We discovered insights not found previously and achieved higher accuracies than LIWC when using our open-vocabulary features in a predictive model, achieving state-of-the-art accuracy in the case of gender prediction. Exploratory analyses like DLA change the PF-04418948MedChemExpress PF-04418948 process from that of testing theories with observations to that of data-driven identification of new connections [97,98]. Our intention here is not a complete replacement for closed-vocabulary analyses like LIWC. When one has a specific theory in mind or a small sample size, an a priori list of words can be ideal; in an open-vocabulary approach, the concept one cares about can be drowned out by more predictive concepts. Further, it may be easier to compare static a priori categories of words across studies. However, automatically clustering words into coherent topics allows one to TF14016 web potentially discover categories that.Ature: 65 to 79 classification accuracy. Of course, such a high/low model cannot directly be used for classifying unlabeled people as one would also need to know who fits in the middle. Regression is a more appropriate predictive task for continuous outcomes like age and personality, even though R scores are naturally smaller than binary classification accuracies. We ran an additional tests to evaluate only those words and phrases, topics, or LIWC categories that are selected via differential language analysis rather than all features. Thus, we used only those language features that significantly correlated (Bonferonnicorrected pv0:001) with the outcome being predicting. To keep consistent with the main evaluation, we used no controls, and so one could view this as a univariate feature selection over each type of feature independently. We again found significant improvement from using the open-vocabulary features over LIWC and no significant changes in accuracy overall. These results are presented in Table S2. In addition to demonstrating the greater informative value of open-vocabulary features, we found our results to be state-of-the-art. The highest previous out-of-sample accuracies for gender prediction based entirely on language were 88.0 over twitter data [68] while our classifiers reach an accuracy of 91.9 . Our increased performance could be attributed to our set of language features, a strong predictive algorithm (the support vector machine), and the large sample of Facebook data.DiscussionOnline social media such as Facebook are a particularly promising resource for the study of people, as “status” updates are self-descriptive, personal, and have emotional content [7]. Language use is objective and quantifiable behavioral data [96], and unlike surveys and questionnaires, Facebook language allows researchers to observe individuals as they freely present themselves in their own words. Differential language analysis (DLA) in social media is an unobtrusive and non-reactive window intothe social and psychological characteristics of people’s everyday concerns. Most studies linking language with psychological variables rely on a priori fixed sets of words, such as the LIWC categories carefully constructed over 20 years of human research [11]. Here, we show the benefits of an open-vocabulary approach in which the words analyzed are based on the data itself. We extracted words, phrases, and topics (automatically clustered sets of words) from millions of Facebook messages and found the language that correlates most with gender, age, and five factors of personality. We discovered insights not found previously and achieved higher accuracies than LIWC when using our open-vocabulary features in a predictive model, achieving state-of-the-art accuracy in the case of gender prediction. Exploratory analyses like DLA change the process from that of testing theories with observations to that of data-driven identification of new connections [97,98]. Our intention here is not a complete replacement for closed-vocabulary analyses like LIWC. When one has a specific theory in mind or a small sample size, an a priori list of words can be ideal; in an open-vocabulary approach, the concept one cares about can be drowned out by more predictive concepts. Further, it may be easier to compare static a priori categories of words across studies. However, automatically clustering words into coherent topics allows one to potentially discover categories that.