Political elites play an important role in the proliferation of online misinformation. However, an understanding of how fact-checking platforms pick up politicized misinformation for fact-checking is still in its infancy. Here, we conduct an empirical analysis of mentions of U.S. political elites within fact-checked statements. For this purpose, we collect a comprehensive dataset consisting of 35,014 true and false statements that have been fact-checked by two major fact-checking organizations (Snopes, PolitiFact) in the U.S. between 2008 and 2023, i.e., within an observation period of 15 years. Subsequently, we perform content analysis and explanatory regression modeling to analyze how veracity is linked to mentions of U.S. political elites in fact-checked statements. Our analysis yields the following main findings: (i) Fact-checked false statements are, on average, 20% more likely to mention political elites than true fact-checked statements. (ii) There is a partisan asymmetry such that fact-checked false statements are 88.1% more likely to mention Democrats, but 26.5% less likely to mention Republicans, compared to fact-checked true statements. (iii) Mentions of political elites in fact-checked false statements reach the highest level during the months preceding elections. (iv) Fact-checked false statements that mention political elites carry stronger other-condemning emotions and are more likely to be pro-Republican, compared to fact-checked true statements. In sum, our study offers new insights into understanding mentions of political elites in false statements on U.S. fact-checking platforms, and bridges important findings at the intersection between misinformation and politicization.

COVID-19 content spreads wildly on social media and produces significant effects in both causing social panic and assisting pandemic management. However, what really enhances the diffusion of pandemic-related content during COVID-19, particularly from the perspective of the content itself, remains unexplored. Using large-scale COVID-19 tweets posted on Twitter, this paper empirically examines the effects of the four key characteristics, namely emotions, topics, hashtags, and mentions, on information spread in the pandemic. The empirical results show that most negative emotions have positive effects on retweeting. Nevertheless, the positive effect of trust on retweeting is unexpectedly the strongest. And the positive effects of the political topics and mentioning politicians further indicate that people are sensitive to the politicization of information during the pandemic. The strongest anger intensity in the political topic also needs to be noticed. The results complement the extant understanding of information diffusion during COVID-19 and provide insights for the governments to understand the psychology and behavior of large population during disasters like global pandemics.

Negative reviews, the poor ratings in postpurchase evaluation, play an indispensable role in e-commerce, especially in shaping future sales and firm equities. However, extant studies seldom examine their potential value for sellers and producers in enhancing capabilities of providing better services and products. For those who exploited the helpfulness of reviews in the view of e-commerce keepers, the ranking approaches were developed for customers instead. To fill this gap, in terms of combining description texts and emotion polarities, the aim of the ranking method in this study is to provide the most helpful negative reviews under a certain product attribute for online sellers and producers. By applying a more reasonable evaluating procedure, experts with related backgrounds are hired to vote for the ranking approaches. Our ranking method turns out to be more reliable for ranking negative reviews for sellers and producers, demonstrating a better performance than the baselines like BM25 with a result of 8% higher. In this paper, we also enrich the previous understandings of emotions in valuing reviews. Specifically, it is surprisingly found that positive emotions are more helpful rather than negative emotions in ranking negative reviews. The unexpected strengthening from positive emotions in ranking suggests that less polarized reviews on negative experience in fact offer more rational feedbacks and thus more helpfulness to the sellers and producers. The presented ranking method could provide e-commerce practitioners with an efficient and effective way to leverage negative reviews from online consumers.

Real estate prices have a significant impact on individuals, families, businesses, and governments. The general objective of real estate price prediction is to identify and exploit socioeconomic patterns arising from real estate transactions over multiple aspects, ranging from the property itself to other contributing factors. However, price prediction is a challenging multidimensional problem that involves estimating many characteristics beyond the property itself. In this paper, we use multiple sources of data to evaluate the economic contribution of different socioeconomic characteristics such as surrounding amenities, traffic conditions and social emotions. Our experiments were conducted on 28,550 houses in Beijing, China and we rank each characteristic by its importance. Since the use of multisource information improves the accuracy of predictions, the aforementioned characteristics can be an invaluable resource to assess the economic and social value of real estate. Code and data are available at: https://github.com/IndigoPurple/PATE.

The study of socioeconomic status has been reformed by the availability of digital records containing data on real estate, points of interest, traffic and social media trends such as micro-blogging. In this paper, we describe a heterogeneous, multi-source, multi-modal, multi-view and multi-distributional dataset named "H4M". The mixed dataset contains data on real estate transactions, points of interest, traffic patterns and micro-blogging trends from Beijing, China. The unique composition of H4M makes it an ideal test bed for methodologies and approaches aimed at studying and solving problems related to real estate, traffic, urban mobility planning, social sentiment analysis etc. The dataset is available at: https://indigopurple.github.io/H4M/index.html.

The tremendous development of online social media have changed people's life fundamentally in recent years. Weibo, a Twitter-like service in China, has attracted more than 500 million users in less than four years and produces more than 100 million Chinese tweets every day. In these massive tweets, different user interests and daily trends are reflected by different topics. While to our best knowledge, a systematic investigation of topic dynamics in Weibo is still missing. Aiming at filling this vital gap, we try to disclose the evolving patterns of topics from the perspective of time, geography, gender, emotion and interaction. First, an incremental learning framework is established to classify more than 200 million tweets into seven topics fast and accurately, whose F-measure arrives as high as 84%. Second, many interesting patterns in topic dynamics are revealed. For instance, happy Entertainment accounts for over half of the tweets and angry Finance possesses the most significant periodic pattern. Besides, the female and male users prefer different topics and Finance shows a surprisingly high correlation between connected users. Finally, our findings could provide insights for the topic-related applications in social media, like event detection or content recommendation.

Study of reciprocity helps to find influential factors for users building relationships, which greatly facilitates the social behavior understanding in trust networks. In the previous literature, the dynamics of both network structure and user generated content are rarely considered. Our investigation of the available timing information from a real-world network demonstrates that time delay has significant impact on reciprocity formation. In particular, we find structural factors possess greater effect on short-term reciprocity while factors based on user generated content become more important for long-term reciprocity. Based on the empirical analysis, we redefine the reciprocity prediction problem as a learning task specific to each pair of users with different reciprocal delays. Evaluations show that our time-aware framework eventually outperforms the conventional classifiers that ignore the temporal information. Meanwhile, we tackle the problem of concept drift through fitting the evolving trend of features for Naive Bayes and performing periodic retraining for Logistic Regression classifiers, respectively.

Recent years have witnessed the explosive growth of online social media. Weibo, a Twitter-like online social network in China, has attracted more than 300 million users in less than three years, with more than 1000 tweets generated in every second. These tweets not only convey the factual information, but also reflect the emotional states of the authors, which are very important for understanding user behaviors. However, a tweet in Weibo is extremely short and the words it contains evolve extraordinarily fast. Moreover, the Chinese corpus of sentiments is still very small, which prevents the conventional keyword-based methods from being used. In light of this, we build a system called MoodLens, which to our best knowledge is the first system for sentiment analysis of Chinese tweets in Weibo. In MoodLens, 95 emoticons are mapped into four categories of sentiments, i.e. angry, disgusting, joyful, and sad, which serve as the class labels of tweets. We then collect over 3.5 million labeled tweets as the corpus and train a fast Naive Bayes classifier, with an empirical precision of 64.3%. MoodLens also implements an incremental learning method to tackle the problem of the sentiment shift and the generation of new words. Using MoodLens for real-time tweets obtained from Weibo, several interesting temporal and spatial patterns are observed. Also, sentiment variations are well captured by MoodLens to effectively detect abnormal events in China. Finally, by using the highly efficient Naive Bayes classifier, MoodLens is capable of online real-time sentiment monitoring. The demo of MoodLens can be found at http://goo.gl/8DQ65.