Thousands of users post on Reddit every day, but a fifth of all posts are removed. How do users react to these removals? We conducted a survey of 907 Reddit users, asking them to reflect on their post removal a few hours after it happened. Examining the qualitative and quantitative responses from this survey, we present users' perceptions of the platform's moderation processes. We find that although roughly a fifth (18%) of the participants accepted that their post removal was appropriate, a majority of the participants did not --- over a third (37%) of the participants did not understand why their post was removed, and further, 29% of the participants expressed some level of frustration about the removal. We focus on factors that shape users' attitudes aboutfairness in moderation andposting again in the community. Our results indicate that users who read community guidelines or receive explanations for removal are more likely to perceive the removal as fair and post again in the future. We discuss implications for moderation practices and policies. Our findings suggest that the extra effort required to establish community guidelines and educate users with helpful feedback is worthwhile, leading to better user attitudes about fairness and propensity to post again.

With the increasing reliance on social media as a dominant communication medium for current news and personal communications, communicators are capable of executing deception with relative ease. While past-related research has investigated written deception in traditional forms of computer mediated communication (e.g. email), we are interested determining if those same indicators hold in social media-like communication and if new, social-media specific linguistic cues to deception exist. Our contribution is two-fold: 1) we present results on human subjects experimentation to confirm existing and new linguistic cues to deception; 2) we present results on classifying deception from training machine learning classifiers using our best features to achieve an average 90% accuracy in cross fold validation.

Although a large body of work has previously investigated various cues predicting deceptive communications, especially as demonstrated through written and spoken language (e.g., [30]), little has been done to explore predicting kinds of deception. We present novel work to evaluate the use of textual cues to discriminate between deception strategies (such as exaggeration or falsification), concentrating on intentionally untruthful statements meant to persuade in a social media context. We conduct human subjects experimentation wherein subjects were engaged in a conversational task and then asked to label the kind(s) of deception they employed for each deceptive statement made. We then develop discriminative models to understand the difficulty between choosing between one and several strategies. We evaluate the models using precision and recall for strategy prediction among 4 deception strategies based on the most relevant psycholinguistic, structural, and data-driven cues. Our single strategy model results demonstrate as much as a 58% increase over baseline (random chance) accuracy and we also find that it is more difficult to predict certain kinds of deception than others.

The increasing proliferation of social media results in users that are forced to ascertain the truthfulness of information that they encounter from unknown sources using a variety of indicators (e.g. explicit ratings, profile information, etc.). Through human-subject experimentation with an online social network-style platform, our study focuses on the determination of credibility in ego-centric networks based on subjects observing social network properties such as degree centrality and geodesic distance. Using manipulated social network graphs, we find that corroboration and degree centrality are most utilized by subjects as indicators of credibility. We discuss the implications of the use of social network graph structural properties and use principal components analysis to visualize the reduced dimensional space.

Large amounts of data are essential for training statistical machine translation systems. In this paper we show how training data can be expanded by paraphrasing one side of a parallel corpus. The new data is made by parsing then generating using an open-source, precise HPSG-based grammar. This gives sentences with the same meaning, but with minor variations in lexical choice and word order. In experiments paraphrasing the English in the Tanaka Corpus, a freely-available Japanese-English parallel corpus, we show consistent, statistically-significant gains on training data sets ranging from 10,000 to 147,000 sentence pairs in size as evaluated by the BLEU and METEOR automatic evaluation metrics.