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.

Web data and computational models can play important roles in analyzing cultural trends. The current study presents an analysis of 23,492 sneaker images and metadata collected from a global reselling shop, StockX.com. Based on data encompassing 22 years from 1999 to 2020, we propose a sneaker design index that helps track changes in the design characteristics of sneakers using a contrastive learning method. Our data suggest that sneaker designs have been employing brighter colors and lower hue and saturation values over time. We also observe how popular brands have continued to build their unique identities in shape-related design space. The embedding analysis also predicts which sneakers will likely see a high premium in the reselling market, suggesting viable algorithm-driven investment and design strategies. The current work is one of the first publicly available studies to analyze product design evolution over a long historical period and has implications for the novel use of Web data to understand cultural patterns that are otherwise difficult to assess.

Unsupervised image clustering methods often introduce alternative objectives to indirectly train the model and are subject to faulty predictions and overconfident results. To overcome these challenges, the current research proposes an innovative model RUC that is inspired by robust learning. RUC’s novelty is at utilizing pseudo-labels of existing image clustering models as a noisy dataset that may include misclassified samples. Its retraining process can revise misaligned knowledge and alleviate the overconfidence problem in predictions. The model’s flexible structure makes it possible to be used as an add-on module to other clustering methods and helps them achieve better performance on multiple datasets. Extensive experiments show that the proposed model can adjust the model confidence with better calibration and gain additional robustness against adversarial noise.

Fact-checking has become the de facto solution for fighting fake news online. This research brings attention to the unexpected and diminished effect of fact-checking due to cognitive biases. We experimented (66,870 decisions) comparing the change in users’ stance toward unproven claims before and after being presented with a hypothetical fact-checked condition. We found that, first, the claims tagged with the ‘Lack of Evidence’ label are recognized similarly as false information unlike other borderline labels, indicating the presence of uncertainty-aversion bias in response to insufficient information. Second, users who initially show disapproval toward a claim are less likely to correct their views later than those who initially approve of the same claim when opposite fact-checking labels are shown — an indication of disapproval bias. Finally, user interviews revealed that users are more likely to share claims with Divided Evidence than those with Lack of Evidence among borderline messages, reaffirming the presence of uncertainty-aversion bias. On average, we confirm that fact-checking helps users correct their views and reduces the circulation of falsehoods by leading them to abandon extreme views. Simultaneously, the presence of two biases reveals that fact-checking does not always elicit the desired user experience and that the outcome varies by the design of fact-checking messages and people’s initial view. These new observations have direct implications for multiple stakeholders, including platforms, policy-makers, and online users.

Precision psychiatry is a new research field that uses advanced data mining over a wide range of neural, behavioral, psychological, and physiological data sources for classification of mental health conditions. This study presents a computational framework for predicting sleep efficiency of insomnia sufferers. A smart band experiment is conducted to collect heterogeneous data, including sleep records, daily activities, and demographics, whose missing values are imputed via Improved Generative Adversarial Imputation Networks (Imp-GAIN). Equipped with the imputed data, we predict sleep efficiency of individual users with a proposed interpretable LSTM-Attention (LA Block) neural network model. We also propose a model, Pairwise Learning-based Ranking Generation (PLRG), to rank users with high insomnia potential in the next day. We discuss implications of our findings from the perspective of a psychiatric practitioner. Our computational framework can be used for other applications that analyze and handle noisy and incomplete time-series human activity data in the domain of precision psychiatry.

As people around the world are spending increasing amounts of time online, the question of how online experiences are linked to health and well-being is essential. This paper presents how activities on Facebook are associated with the depressive states of users. Based on online logs of 212 young adults, we show not only the sheer size of the network but also the frequency and diversity of interactions on social networks have close associations with depression. Depressed individuals reported smaller involved networks regarding comments and likes, the two popular forms of interactions. In contrast to the decreased level of interactions, depressed individuals showed an increase in the wall post rates and were active online during midday, which can be interpreted as an endemic behavior linked to the perceived degree of loneliness among young adults who are avid users of social media. We discuss these findings from theoretical, empirical, and subjective perspectives.