Two types of topic modeling predominate: generative methods that employ probabilistic latent models and clustering methods that identify semantically coherent groups.This paper newly presents UTopic (Unified neural Topic model via contrastive learning and term weighting) that combines the advantages of these two types.UTopic uses contrastive learning and term weighting to learn knowledge from a pretrained language model and discover influential terms from semantically coherent clusters.Experiments show that the generated topics have a high-quality topic-word distribution in terms of topic coherence, outperforming existing baselines across multiple topic coherence measures.We demonstrate how our model can be used as an add-on to existing topic models and improve their performance.

Wikipedia plays a crucial role in the integrity of the Web. This work analyzes the reliability of this global encyclopedia through the lens of its references. We operationalize the notion of reference quality by defining reference need (RN), i.e., the percentage of sentences missing a citation, and reference risk (RR), i.e., the proportion of non-authoritative references. We release Citation Detective, a tool for automatically calculating the RN score, and discover that the RN score has dropped by 20 percent point in the last decade, with more than half of verifiable statements now accompanying references. The RR score has remained below 1% over the years as a result of the efforts of the community to eliminate unreliable references. We propose pairing novice and experienced editors on the same Wikipedia article as a strategy to enhance reference quality. Our quasi-experiment indicates that such a co-editing experience can result in a lasting advantage in identifying unreliable sources in future edits. As Wikipedia is frequently used as the ground truth for numerous Web applications, our findings and suggestions on its reliability can have a far-reaching impact. We discuss the possibility of other Web services adopting Wiki-style user collaboration to eliminate unreliable content.

In a Big Data era, a lot of open data set is published and shared with the public. That creates new services and business. However, the publication may cause a leakage problem of private information. In general, de-identification techniques are applied to the data before publication. The problem, however, has not been solved completely. Personal data can be obtained from the several sources such as Internet service and social media. In this situation, a de-identified open data may be simply joined with the leaked external data and it may result in a re-identification issue. We propose a new analytic model to measure the personal information leakage risk in the open data before publishing. The proposed model formulates the entropy-based re-identification risk to measure the privacy leakage risk. We also try to find the data utility measure by using the entropy while preserving the privacy. Based on both the risk and the utility measure, we propose the guideline for data open to the public. We show the guideline including the risk and utility measurement can be applicable with the empirical experiments.