Predicting Dark Triad Personality Traits from Twitter Usage and a Linguistic Analysis of Tweets

Sumner, Chris; Byers, Alison; Boochever, Rachel; Park, Gregory J.

doi:10.1109/icmla.2012.218

Cited by 211 publications

(142 citation statements)

References 17 publications

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“…example findings (a small but diverse selection across a vast literature) include older individuals using more first-person plurals and future-tense verbs in spoken interview transcripts (Pennebaker and Stone 2003), males swearing more across a variety of domains (Newman et al 2008), couples having more stable relationships if they match pronoun category usage (Ireland et al 2011), and highly open individuals including more quotations in social media (Sumner et al 2012).…”

Section: Manual Dictionariesmentioning

confidence: 98%

Data-Driven Content Analysis of Social Media

Schwartz¹,

Ungar²

2015

The ANNALS of the American Academy of Political and Social Scie

133

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researchers have long measured people's thoughts, feelings, and personalities using carefully designed survey questions, which are often given to a relatively small number of volunteers. the proliferation of social media, such as twitter and Facebook, offers alternative measurement approaches: automatic content coding at unprecedented scales and the statistical power to do open-vocabulary exploratory analysis. we describe a range of automatic and partially automatic content analysis techniques and illustrate how their use on social media generates insights into subjective wellbeing, health, gender differences, and personality.

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Section: Manual Dictionariesmentioning

confidence: 98%

Data-Driven Content Analysis of Social Media

Schwartz¹,

Ungar²

2015

The ANNALS of the American Academy of Political and Social Scie

133

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“…For example, researchers have correlated word use on blogs with personality [19,61]. See [20,21,52] for similar personality analyses based on Facebook and Twitter. The approach used by these researchers was to utilize collections of thematic dictionaries such as Linguistic Inquiry and Word Count (LIWC) [40] and calculate word counts for different dictionaries.…”

Section: Measuring Employee Engagementmentioning

confidence: 99%

Inferring Employee Engagement from Social Media

Shami

Müller

Pal

et al. 2015

Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems

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Employees increasingly are expressing ideas and feelings through enterprise social media. Recent work in CHI and CSCW has applied linguistic analysis towards understanding employee experiences. In this paper, we apply dictionary based linguistic analysis to measure 'Employee Engagement'. Employee engagement is a measure of employee willingness to apply discretionary effort towards organizational goals, and plays an important role in organizational outcomes such as financial or operational results. Organizations typically use surveys to measure engagement. This paper describes an approach to model employee engagement based on word choice in social media. This method can potentially complement surveys, thus providing more real-time insights into engagement and allowing organizations to address engagement issues faster. Our results predicting engagement scores on a survey by combining demographics with social media text demonstrate that social media text has significant predictive power compared to demographic data alone. We also find that engagement may be a state than a stable trait since social media posts closer to the administration of the survey had the most predictive power. We further identify the minimum number of social media posts required per employee for the best prediction.

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“…In a previous experiment, called the Twitter Big Five Experiment [15], [21] (relevant because it uses almost the same independent features as the present work, although the class value differs), a number of users were solicited to take an online personality survey, rating these users according to the Big Five personality index (Agreeableness, Conscientiousness, Extroversion, Openness, Neuroticism) and the Dark Triad of negative personality traits (Narcissism, Machiavellianism, and Psychopathy). In addition, three classes of features were extracted from each individual's profile: demographic features, linguistic features, and count-based features.…”

Section: Case Studymentioning

confidence: 99%

How the Choice of Wrapper Learner and Performance Metric Affects Subset Evaluation

Wald

Khoshgoftaar

Napolitano

2013

2013 IEEE 25th International Conference on Tools With Artificial Intelligence

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Due to the widespread problem of high dimensionality (datasets with many features/independent attributes), feature selection has become an important research topic in many areas of machine learning. One form of feature selection, wrapper-based subset evaluation, has been the focus of a moderate amount of research, because its use of classification learners to find optimal feature subsets has the potential to remove redundant features and find feature subsets which directly achieve the goal of improving classification performance. However, while the choice of learner to use within the wrapper framework has previously been studied, no paper has thoroughly investigated the role of the performance metric used within the wrapper process. Especially with imbalanced data (data where one class predominates over other classes), traditional metrics such as accuracy can give a misleading view of how many instances from each class are mislabeled. While it seems intuitive that metrics which take balance into account will affect the chosen features, no previous study has investigated this effect directly. In the present work, we test five different learners and five different performance metrics within the wrapper framework and use a newly-proposed variant of the Tanimoto Index to evaluate the similarity among the different choices of learner and metric as all other factors are held constant, using two datasets from the domain of social network profile mining. We find that while the Best Arithmetic Mean and Best Geometric Mean metrics (both of which find the stated means of True Positive Rate and True Negative Rate) are somewhat similar, they still are quite distinct, and no other metrics are particularly similar to one another. The five learners were also found to produce extremely dissimilar feature subsets. Thus, we show that the choice of both learner and metric has a major effect on which features are selected by through wrapper-based feature selection.

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Predicting Dark Triad Personality Traits from Twitter Usage and a Linguistic Analysis of Tweets

Cited by 211 publications

References 17 publications

Data-Driven Content Analysis of Social Media

Data-Driven Content Analysis of Social Media

Inferring Employee Engagement from Social Media

How the Choice of Wrapper Learner and Performance Metric Affects Subset Evaluation

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