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Detecting People Interested in Non-Suicidal Self-Injury on Social Media

We propose a supervised learning approach to detect people interested in Non-Suicidal Self-Injury (NSSI). We treat the task as a binary classification problem, and build classifiers based upon features extracted from people self-declared interests. Experimental evaluation on a real-world dataset, the LiveJournal social blogging networking platform, demonstrates the effectiveness of our proposed model.

We assume that any LiveJournal user who is a member of one of the 139 manually selected NSSI-related thematic communities or contributes to such community by posting or commenting, is interested in NSSI. We designate such users as "harmers" for brevity, acknowledging that some may not practice self-injury. Following the harmers' friendship network, we further collect some of their immediate friends and friends-offriends (the "non-harmers"), chosen randomly to match the size and age of the harmers.
The dataset has 11,972 harmers, 12,600 friends, 11,672 friends-of-friends, and 1,264 distinct self-declared single-or multi-word interests (on average, 26.2 interests per user). These self-declared interests serve as virtual profiles for each user. We regard each interest as a feature and represent the feature vectors in the following three ways: Simple-Count-Vector: The feature value of each interest is represented by their occurrence frequency in user profiles. TF-IDF-Vector: Each feature is represented by its TF-IDF value. Topic-Distribution-Vector: We apply the Latent Dirichlet Allocation (LDA) model to learn a topic distribution over each user profile. We use the distribution as the feature vector. We choose the number of topics to be 10.

Results
We treat the harmers as positive samples and the non-harmers as negative samples.
Since the original data set is unbalanced (with 1/3 positive and 2/3 negative samples), we pick 11,972 negative samples uniformly at random to form a balanced data set, and only consider those interests that are declared by more than 100 but fewer than 16,760 (70% of all users in the balanced data set) users to construct the three feature vectors mentioned above. We repeat the random sampling process five times and apply 5-fold cross-validation to evaluate the model's performance. Table 1 shows the classification results evaluated by accuracy, precision, recall, and F 1 score using the three feature vector representations and Naïve Bayes (NB) and Logistic Regression (LR) classifiers. We achieved up to 73% accuracy, 77% precision, 67% recall, and 71% F 1 score. For both Naïve Bayes and Logistic Regression classifiers, using TF-IDF feature vectors increases recall and F 1 score but slightly decreases precision as compared to Simple-Count vectors. Since our project aims to detect concealed behavior, higher recall and false positives are more desirable than a higher precision and false negatives.
We compute and sort each feature by its odds-ratio value learned from Naïve Bayes and report the top 20 positive and negative features. Table 2 shows the top 20 positive interests prevalent among the harmers (such as cutting, burning, self-injury, and selfharm) and the bottom 20 interests that the harmers tend to neglect (such as programming, linux, architecture, and chess). The harmers' interests are undoubtedly specific to NSSI, related disorders, and mental health in general. The non-harmers interests seem to be aligned with an adolescent/young adult's interests in the early XXI st century. Summary. In this paper, we propose a supervised learning approach to detect people interested in NSSI. Experimental evaluation on a real-world dataset-the LiveJournal social blogging networking platform-demonstrates our proposed model's effectiveness. For future work, in addition to the pure content-based features, we would like to integrate network, demographic, sentimental, contextual, and other features. We consider building and comparing classifiers based on different algorithms (including logistic regression, random forest, and artificial neural networks). We plan to apply our approach to other social media platforms such as Twitter or Facebook.