arXiv subjects

Jichang Zhao

Publications and source records attributed to Jichang Zhao.

At least 37 records · Page 2Linked to original sources

arXiv2019

Fake news propagate differently from real news even at early stages of spreading

Social media can be a double-edged sword for society, either as a convenient channel exchanging ideas or as an unexpected conduit circulating fake news through a large population. While existing studies of fake news focus on theoretical modeling of propagation or identification methods based on machine learning, it is important to understand the realistic mechanisms between theoretical models and black-box methods. Here we track large databases of fake news and real news in both, Weibo in China and Twitter in Japan from different culture, which include their complete traces of re-postings. We find in both online social networks that fake news spreads distinctively from real news even at early stages of propagation, e.g. five hours after the first re-postings. Our finding demonstrates collective structural signals that help to understand the different propagation evolution of fake news and real news. Different from earlier studies, identifying the topological properties of the information propagation at early stages may offer novel features for early detection of fake news in social media.

physics.soc-ph↗

arXiv2018

Aggregating multiple types of complex data in stock market prediction: A model-independent framework

The increasing richness in volume, and especially types of data in the financial domain provides unprecedented opportunities to understand the stock market more comprehensively and makes the price prediction more accurate than before. However, they also bring challenges to classic statistic approaches since those models might be constrained to a certain type of data. Aiming at aggregating differently sourced information and offering type-free capability to existing models, a framework for predicting stock market of scenarios with mixed data, including scalar data, compositional data (pie-like) and functional data (curve-like), is established. The presented framework is model-independent, as it serves like an interface to multiple types of data and can be combined with various prediction models. And it is proved to be effective through numerical simulations. Regarding to price prediction, we incorporate the trading volume (scalar data), intraday return series (functional data), and investors' emotions from social media (compositional data) through the framework to competently forecast whether the market goes up or down at opening in the next day. The strong explanatory power of the framework is further demonstrated. Specifically, it is found that the intraday returns impact the following opening prices differently between bearish market and bullish market. And it is not at the beginning of the bearish market but the subsequent period in which the investors' "fear" comes to be indicative. The framework would help extend existing prediction models easily to scenarios with multiple types of data and shed light on a more systemic understanding of the stock market.

q-fin.CP↗

arXiv2018

Herding boosts too-connected-to-fail risk in stock market of China

The crowd panic and its contagion play non-negligible roles at the time of the stock crash, especially for China where inexperienced investors dominate the market. However, existing models rarely consider investors in networking stocks and accordingly miss the exact knowledge of how panic contagion leads to abrupt crash. In this paper, by networking stocks of sharing common mutual funds, a new methodology of investigating the market crash is presented. It is surprisingly revealed that the herding, which origins in the mimic of seeking for high diversity across investment strategies to lower individual risk, will produce too-connected-to-fail stocks and reluctantly boosts the systemic risk of the entire market. Though too-connected stocks might be relatively stable during the crisis, they are so influential that a small downward fluctuation will cascade to trigger severe drops of massive successor stocks, implying that their falls might be unexpectedly amplified by the collective panic and result in the market crash. Our findings suggest that the whole picture of portfolio strategy has to be carefully supervised to reshape the stock network.

q-fin.GN↗

arXiv2018

Repetitive users network emerges from multiple rumor cascades

Rumor spreading on online social media is presenting a significant threat to society of post-truth epoch. Extensive efforts have been devoted to rumor identification and debunking, assuming that a specific rumor propagation is a single event network and neglecting possible interdependence between different rumor cascades. Here we study the collective propagation of multiple rumors, and surprisingly find a network of users that repeatedly participate in different rumor cascades. Though these repetitive users demonstrate minor difference at the level of single propagation network, they are found to form a significantly more intensive collaboration network from multiple rumor cascades compared to news propagation. The clique-like cluster formed by repetitive rumor spreaders can serve as a high quality feature for rumor identification and blocking targets for rumor prevention. Our findings can provide a better understanding of rumor spread by viewing multiple rumor propagations as one interacting rumor ecosystem, and suggest novel methods for distinguishing and mitigation based on rumor spreading history.

physics.soc-ph↗

arXiv2018

Workforce migration and its economic implications: A perspective from social media in China

The workforce remains the most basic element of social production, even in modern societies. Its migration, especially for developing economies such as China, plays a strong role in the reallocation of productive resources and offers a promising proxy for understanding socio-economic issues. Nevertheless, due to long cycle, expensive cost and coarse granularity, conventional surveys face challenges in comprehensively profiling it. With the permeation of smart and mobile devices in recent decades, booming social media has essentially broken spatio-temporal constraints and enabled the continuous sensing of the real-time mobility of massive numbers of individuals. In this study, we demonstrate that similar to a natural shock, the Spring Festival culturally drives workforce travel between workplaces and hometowns, and the trajectory footprints from social media therefore open a window with unparalleled richness and fine granularity to explore laws in national-level workforce migration. To understand the core driving forces of workforce migration flux between cities in China, various indicators reflecting the benefits and costs of migration are introduced into our prediction model. We find that urban GDP (gross domestic product) and travel time are two excellent indicators to help make predictions. Diverse migration patterns are then revealed by clustering the trajectories, which give important clues to help understand the different roles of Chinese cities in their own development and in regional economic development. These patterns are further explained as a joint effect of the subjective will to seek personal benefits and the capacity requirements of local labour markets. Our study implies that the non-negligible entanglement between social media and macroeconomic behaviours can be insightful for policymaking in social-economic issues.

cs.SI↗

arXiv2018

The Power of Trading Polarity: Evidence from China Stock Market Crash

The imbalance of buying and selling functions profoundly in the formation of market trends, however, a fine-granularity investigation of the imbalance is still missing. This paper investigates a unique transaction dataset that enables us to inspect the imbalance of buying and selling on the man-times level at high frequency, what we call 'trading polarity', for a large cross-section of stocks from Shenzhen Stock Exchange. The trading polarity measures the market sentiment toward stocks from a view of very essence of trading desire. When using the polarity to examine market crash, we find that trading polarity successfully reflects the changing of market-level behavior in terms of its flipping times, depth, and length. We further investigate the relationship between polarity and return. At market-level, trading polarity is negatively correlated with returns, while at stock-level, this correlation changes according to market conditions, which becomes a good signal of market psychology transition. Also, the significant correlation disclosed by the market polarity and market emotion implies that our presented polarity, which essentially calculated in the context of high-frequency trading data, can real-timely reflect the sentiment of the market. The trading polarity indeed provides a new way to understand and foresee the market behavior.

q-fin.CP↗

arXiv2017

Homophily of Music Listening in Online Social Networks

Homophily, ranging from demographics to sentiments, breeds connections in social networks, either offline or online. However, with the prosperous growth of music streaming service, whether homophily exists in online music listening remains unclear. In this study, two online social networks of a same group of active users are established respectively in Netease Music and Weibo. Through presented multiple similarity measures, it is evidently demonstrated that homophily does exist in music listening of both online social networks. The unexpected music similarity in Weibo also implies that knowledge from generic social networks can be confidently transfered to domain-oriented networks for context enrichment and algorithm enhancement. Comprehensive factors that might function in formation of homophily are further probed and many interesting patterns are profoundly revealed. It is found that female friends are more homogeneous in music listening and positive and energetic songs significantly pull users close. Our methodology and findings would shed lights on realistic applications in online music services.

cs.SI↗

arXiv2017

An agent-based model for emotion contagion and competition in online social media

Recent studies suggest that human emotions diffuse in not only real-world communities but also online social media. More and more mechanisms beyond emotion contagion are revealed, including emotion correlations which indicate their influence and the coupling of emotion diffusion and network structure such as tie strength. Besides, different emotions might even compete in shaping the public opinion. However, a comprehensive model that considers up-to-date findings to replicate the patterns of emotion contagion in online social media is still missing. In this paper, to bridge this vital gap, we propose an agent-based emotion contagion model which combines features of emotion influence and tie strength preference in the dissemination process. The simulation results indicate that anger-dominated users have higher vitality than joy-dominated ones, and anger prefers weaker ties than joy in diffusion, which could make it easier to spread between online groups. Moreover, anger's high influence makes it competitive and easily to dominate the community, especially when negative public events occur. It is also surprisingly revealed that as the ratio of anger approaches joy with a gap less than 10%, angry tweets and users will eventually dominate the online social media and arrives the collective outrage in the cyber space. The critical gap disclosed here can be indeed warning signals at early stages for outrage controlling in online social media. All the parameters of the presented model can be easily estimated from the empirical observations and their values from historical data could help reproduce the emotion contagion of different circumstances. Our model would shed lights on the study of multiple issues like forecasting of emotion contagion in terms of computer simulations.

cs.SI↗

arXiv2017

Tales of Emotion and Stock in China: Volatility, Causality and Prediction

How the online social media, like Twitter or its variant Weibo, interacts with the stock market and whether it can be a convincing proxy to predict the stock market have been debated for years, especially for China. As the traditional theory in behavioral finance states, the individual emotions can influence decision-makings of investors, it is reasonable to further explore these controversial topics systematically from the perspective of online emotions, which are richly carried by massive tweets in social media. Through thorough studies on over 10 million stock-relevant tweets and 3 million investors from Weibo, it is revealed that inexperienced investors with high emotional volatility are more sensible to the market fluctuations than the experienced or institutional ones, and their dominant occupation also indicates that the Chinese market might be more emotional as compared to its western counterparts. Then both correlation analysis and causality test demonstrate that five attributes of the stock market in China can be competently predicted by various online emotions, like disgust, joy, sadness and fear. Specifically, the presented prediction model significantly outperforms the baseline model, including the one taking purely financial time series as input features, on predicting five attributes of the stock market under the $K$-means discretization. We also employ this prediction model in the scenario of realistic online application and its performance is further testified.

cs.CY↗

arXiv2017

Extroverts Tweet Differently from Introverts in Weibo

Being dominant factors driving the human actions, personalities can be excellent indicators in predicting the offline and online behavior of different individuals. However, because of the great expense and inevitable subjectivity in questionnaires and surveys, it is challenging for conventional studies to explore the connection between personality and behavior and gain insights in the context of large amount individuals. Considering the more and more important role of the online social media in daily communications, we argue that the footprint of massive individuals, like tweets in Weibo, can be the inspiring proxy to infer the personality and further understand its functions in shaping the online human behavior. In this study, a map from self-reports of personalities to online profiles of 293 active users in Weibo is established to train a competent machine learning model, which then successfully identifies over 7,000 users as extroverts or introverts. Systematical comparisons from perspectives of tempo-spatial patterns, online activities, emotion expressions and attitudes to virtual honor surprisingly disclose that the extrovert indeed behaves differently from the introvert in Weibo. Our findings provide solid evidence to justify the methodology of employing machine learning to objectively study personalities of massive individuals and shed lights on applications of probing personalities and corresponding behaviors solely through online profiles.

cs.CY↗

arXiv2017

A GPU-Based Solution to Fast Calculation of Betweenness Centrality on Large Weighted Networks

Recent decades have witnessed the tremendous development of network science, which indeed brings a new and insightful language to model real systems of different domains. Betweenness, a widely employed centrality in network science, is a decent proxy in investigating network loads and rankings. However, the extremely high computational cost greatly prevents its applying on large networks. Though several parallel algorithms have been presented to reduce its calculation cost on unweighted networks, a fast solution for weighted networks, which are in fact more ubiquitous than unweighted ones in reality, is still missing. In this study, we develop an efficient parallel GPU-based approach to boost the calculation of betweenness centrality on very large and weighted networks. Comprehensive and systematic evaluations on both synthetic and real-world networks demonstrate that our solution can arrive the performance of 30x to 150x speedup over the CPU implementation by integrating the work-efficient and warp-centric strategies. Our algorithm is completely open-sourced and free to the community and it is public available through https://dx.doi.org/10.6084/m9.figshare.4542405. Considering the pervasive deployment and declining price of GPU on personal computers and servers, our solution will indeed offer unprecedented opportunities for exploring the betweenness related problems in network science.

cs.SI↗

arXiv2016

Higher contagion and weaker ties mean anger spreads faster than joy in social media

Increasing evidence suggests that, similar to face-to-face communications, human emotions also spread in online social media. However, the mechanisms underlying this emotional contagion, for example, whether different feelings spread in unlikely ways or how the spread of emotions relates to the social network, is rarely investigated. Indeed, because of high costs and spatio-temporal limitations, explorations of this topic are challenging using conventional questionnaires or controlled experiments. Because they are collection points for natural affective responses of massive individuals, online social media sites offer an ideal proxy for tackling this issue from the perspective of computational social science. In this paper, based on the analysis of millions of tweets in Weibo, surprisingly, we find that anger is more contagious than joy, indicating that it can spark more angry follow-up tweets. Moreover, regarding dissemination in social networks, anger travels easily along weaker ties than joy, meaning that it can infiltrate different communities and break free of local traps because strangers share such content more often. Through a simple diffusion model, we reveal that greater contagion and weaker ties function cooperatively to speed up anger's spread. The diffusion of real-world events with different dominant emotions provides further testimony to the findings. To the best of our knowledge, this is the first time that quantitative long-term evidence has been presented that reveals a difference in the mechanism by which joy and anger are disseminated. Our findings shed light on both personal anger management in human communications and on controlling collective outrage in cyberspace.

cs.SI↗

arXiv2016

Sleeping Beauties in Meme Diffusion

A sleeping beauty in diffusion indicates that the information, can be ideas or innovations, will experience a hibernation before a sudden spike of popularity and it is widely found in citation history of scientific publications. However, in this study, we demonstrate that the sleeping beauty is an interesting and unexceptional phenomenon in information diffusion and even more inspiring, there exist two consecutive sleeping beauties in the entire lifetime of propagation, suggesting that the information, including scientific topics, search queries or Wikipedia entries, which we call memes, will go unnoticed for a period and suddenly attracts some attention, and then it falls asleep again and later wakes up with another unexpected popularity peak. Further explorations on this phenomenon show that intervals between two wake ups follow an exponential distribution and the second wake up generally reaches its peak at a higher velocity. In addition, higher volume of the first wake up will lead to even much higher popularity of the second wake up with great odds. Taking these findings into consideration, an upgraded Bass model is presented to well describe the diffusion dynamics of memes on different media. Our results can help understand the common mechanism behind propagation of different memes and are instructive to locate the tipping point in marketing or find innovative publications in science.

cs.SI↗

arXiv2016

Can Online Emotions Predict the Stock Market in China?

Whether the online social media, like Twitter or its variant Weibo, can be a convincing proxy to predict the stock market has been debated for years, especially for China. However, as the traditional theory in behavioral finance states, the individual emotions can influence decision-making of investors, so it is reasonable to further explore this controversial topic from the perspective of online emotions, which is richly carried by massive tweets in social media. Surprisingly, through thorough study on over 10 million stock-relevant tweets from Weibo, both correlation analysis and causality test show that five attributes of the stock market in China can be competently predicted by various online emotions, like disgust, joy, sadness and fear. Specifically, the presented model significantly outperforms the baseline solutions on predicting five attributes of the stock market under the $K$-means discretization. We also employ this model in the scenario of realistic online application and its performance is further testified.

cs.SI↗

arXiv2015

Spatio-temporal propagation of cascading overload failures

Different from the direct contact in epidemics spread, overload failures propagate through hidden functional dependencies. Many studies focused on the critical conditions and catastrophic consequences of cascading failures. However, to understand the network vulnerability and mitigate the cascading overload failures, the knowledge of how the failures propagate in time and space is essential but still missing. Here we study the spatio-temporal propagation behavior of cascading overload failures analytically and numerically. The cascading overload failures are found to spread radially from the center of the initial failure with an approximately constant velocity. The propagation velocity decreases with increasing tolerance, and can be well predicted by our theoretical framework with one single correction for all the tolerance values. This propagation velocity is found similar in various model networks and real network structures. Our findings may help to predict and mitigate the dynamics of cascading overload failures in realistic systems.

physics.soc-ph↗

arXiv2015

Emoticon-based Ambivalent Expression: A Hidden Indicator for Unusual Behaviors in Weibo

Recent decades have witnessed online social media being a big-data window for quantificationally testifying conventional social theories and exploring much detailed human behavioral patterns. In this paper, by tracing the emoticon use in Weibo, a group of hidden "ambivalent users" are disclosed for frequently posting ambivalent tweets containing both positive and negative emotions. Further investigation reveals that this ambivalent expression could be a novel indicator of many unusual social behaviors. For instance, ambivalent users with the female as the majority like to make a sound in midnights or at weekends. They mention their close friends frequently in ambivalent tweets, which attract more replies and thus serve as a more private communication way. Ambivalent users also respond differently to public affairs from others and demonstrate more interests in entertainment and sports events. Moreover, the sentiment shift of words adopted in ambivalent tweets is more evident than usual and exhibits a clear "negative to positive" pattern. The above observations, though being promiscuous seemingly, actually point to the self regulation of negative mood in Weibo, which could find its base from the emotion management theories in sociology but makes an interesting extension to the online environment. Finally, as an interesting corollary, ambivalent users are found connected with compulsive buyers and turn out to be perfect targets for online marketing.

cs.SI↗

arXiv2014

Word Network Topic Model: A Simple but General Solution for Short and Imbalanced Texts

The short text has been the prevalent format for information of Internet in recent decades, especially with the development of online social media, whose millions of users generate a vast number of short messages everyday. Although sophisticated signals delivered by the short text make it a promising source for topic modeling, its extreme sparsity and imbalance brings unprecedented challenges to conventional topic models like LDA and its variants. Aiming at presenting a simple but general solution for topic modeling in short texts, we present a word co-occurrence network based model named WNTM to tackle the sparsity and imbalance simultaneously. Different from previous approaches, WNTM models the distribution over topics for each word instead of learning topics for each document, which successfully enhance the semantic density of data space without importing too much time or space complexity. Meanwhile, the rich contextual information preserved in the word-word space also guarantees its sensitivity in identifying rare topics with convincing quality. Furthermore, employing the same Gibbs sampling with LDA makes WNTM easily to be extended to various application scenarios. Extensive validations on both short and normal texts testify the outperformance of WNTM as compared to baseline methods. And finally we also demonstrate its potential in precisely discovering newly emerging topics or unexpected events in Weibo at pretty early stages.

cs.CL↗

arXiv2014

Competition Between Homophily and Information Entropy Maximization in Social Networks

In social networks, it is conventionally thought that two individuals with more overlapped friends tend to establish a new friendship, which could be stated as homophily breeding new connections. While the recent hypothesis of maximum information entropy is presented as the possible origin of effective navigation in small-world networks. We find there exists a competition between information entropy maximization and homophily in local structure through both theoretical and experimental analysis. This competition means that a newly built relationship between two individuals with more common friends would lead to less information entropy gain for them. We conjecture that in the evolution of the social network, both of the two assumptions coexist. The rule of maximum information entropy produces weak ties in the network, while the law of homophily makes the network highly clustered locally and the individuals would obtain strong and trust ties. Our findings shed light on the social network modeling from a new perspective.

cs.SI↗