Abstract
This research proposes a new feature extraction algorithm using aggregated user engagements on social media in order to achieve demographics and personality discovery tasks. Our proposed framework can discover seven essential attributes, including gender identity, age group, residential area, education level, political affiliation, religious belief, and personality type. Multiple feature sets are developed, including comment text, community activity, and hybrid features. Various machine learning algorithms are explored, such as support vector machines, random forest, multi-layer perceptron, and naïve Bayes. An empirical analysis is performed on various aspects, including correctness, robustness, training time, and the class imbalance problem. We obtained the highest prediction performance by using our proposed feature extraction algorithm. The result on personality type prediction was 87.18%. For the demographic attribute prediction task, our feature sets also outperformed the baseline at 98.1% for residential area, 94.7% for education level, 92.1% for gender identity, 91.5% for political affiliation, 60.6% for religious belief, and 52.0% for the age group. Moreover, this paper provides the guideline for the choice of classifiers with appropriate feature sets.
Highlights
We looked for users who specified themselves with two-digit ages in their descriptions, which happened to be the same patterns as gender identity, in agerelated communities
For the CA_Freq and CA_Wgt_100 feature set, we found that random oversampling (RO) and SMOTE had a small contribution to the F1 score for education and political belief prediction
We have done an empirical analysis of our proposed feature sets for private attribute prediction covering classification performance, training time, and imbalance problems
Summary
User demographic attributes and personality type (collectively called “private attributes”) can be applied in several domains, for example, hate speech detection [1] and product recommendation [2] using additional demographic data. The ability to identify personality is useful for better understanding ourselves and others. We can choose an appropriate field of study that fits our personality or apply for a job that best fits our preferences. It can be applied by recruiters to find appropriate applicants that fit the job description [3]. Persuasive mass communication is another benefit of personality discovery. It aims at encouraging large groups of people to believe and act on the communicator’s viewpoint. It is used by governments to encourage healthy behaviors, by marketers to acquire and retain consumers, and by political parties to mobilize the voting population [4]
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