In this paper, we introduce a novel sociotechnical moderation system for Reddit called Crossmod. Through formative interviews with 11 active moderators from 10 different subreddits, we learned about the limitations of currently available automated tools, and how a new system could extend their capabilities. Developed out of these interviews, Crossmod makes its decisions based on cross-community learning---an approach that leverages a large corpus of previous moderator decisions via an ensemble of classifiers. Finally, we deployed Crossmod in a controlled environment, simulating real-time conversations from two large subreddits with over 10M subscribers each. To evaluate Crossmod's moderation recommendations, 4 moderators reviewed comments scored by Crossmod that had been drawn randomly from existing threads. Crossmod achieved an overall accuracy of 86% when detecting comments that would be removed by moderators, with high recall (over 87.5%). Additionally, moderators reported that they would have removed 95.3% of the comments flagged by Crossmod; however, 98.3% of these comments were still online at the time of this writing (i.e., not removed by the current moderation system). To the best of our knowledge, Crossmod is the first open source, AI-backed sociotechnical moderation system to be designed using participatory methods.

Keystroke Dynamics or typing dynamics refers to the method of identifying or confirming the identity of a person based on the typing pattern by checking the various timing information obtained when a key is pressed and released. It has been hypothesized that a user's keystroke patterns change according to his/her emotions. However, there were only limited investigations about the phenomenon itself in previous studies. The work in this paper is based on the use of auditory stimuli to check the influence of keystroke patterns and its variations according to the emotions of an individual. The advantages of using this method are that the data collected through this approach is non-intrusive and easy to obtain. The proposed system is of a controlled experiment to collect keystroke data from multiple subjects in a variety of emotional states induced by International Affective Digitized Sounds (IADS) using an Android Application. To examine the data collected, Two-way Valence (3) x Arousal (3) ANOVAs is applied. The work in this paper aims to prove that keystroke duration and latency are influenced by valence and arousal.