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Xin Shuai

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Papers by Xin Shuai

TipMaster: A Knowledge Base of Authoritative Local News Sources on Social Media

Proceedings of the AAAI Conference on Artificial Intelligence

Twitter has become an important online source for real-time news dissemination. Especially, offic... more Twitter has become an important online source for real-time news dissemination. Especially, official accounts of local government and media outlets have provided newsworthy and authoritative information, revealing local trends and breaking news. In this paper, we describe TipMaster an automatically constructed knowledge base of Twitter accounts that are likely to report local news, from government agencies to local media outlets. First, we implement classifiers for detecting these accounts by integrating heterogeneous information from the accounts' textual metadata, profile images, and their tweet messages. Next, we demonstrate two use cases for TipMaster: 1) as a platform that monitors real-time social media messages for local breaking news, and 2) as an authoritative source for verifying nascent rumors. Experimental results show that our account classification algorithms achieve both high precision and recall (around 90%). The demonstrated case studies prove that our platform ...

Does Quantum Interference exist in Twitter?

arXiv (Cornell University), Jul 4, 2011

It becomes more difficult to explain the social information transfer phenomena using the classic ... more It becomes more difficult to explain the social information transfer phenomena using the classic models based merely on Shannon Information Theory (SIT) and Classic Probability Theory (CPT), because the transfer process in the social world is rich of semantic and highly contextualized. This paper aims to use twitter data to explore whether the traditional models can interpret information transfer in social networks, and whether quantum-like phenomena can be spotted in social networks. Our main contributions are: (1) SIT and CPT fail to interpret the information transfer occurring in Twitter; and (2) Quantum interference exists in Twitter, and (3) a mathematical model is proposed to elucidate the spotted quantum phenomena.

format_quoteFound that Twitter information transfer does not always increase with more channels, contradicting Shannon's Information Theory conclusions.format_quote

Quick Check: A Legal Research Recommendation System

Finding relevant sources of law that discuss a specific legal issue and support a favorable decis... more Finding relevant sources of law that discuss a specific legal issue and support a favorable decision is an onerous and time-consuming task for litigation attorneys. In this paper, we present Quick Check, a system that extracts the legal arguments from a user’s brief and recommends highly relevant case law opinions. Using a combination of full-text search, citation network analysis, clickstream analysis, and a hierarchy of ranking models trained on a set of over 10K annotations, the system is able to effectively recommend cases that are similar in both legal issue and facts. Importantly, the system leverages a detailed legal taxonomy and an extensive body of editorial summaries of case law. We demonstrate how recommended cases from the system are surfaced through a user interface that enables a legal researcher to quickly determine the applicability of a case with respect to a given legal issue.

A Multidimensional Investigation of the Effects of Publication Retraction on Scholarly Impact

Journal of the Association for Information Science and Technology, 2017

During the past few decades, the rate of publication retractions has increased dramatically in ac... more During the past few decades, the rate of publication retractions has increased dramatically in academia. In this study, we investigate retractions from a quantitative perspective, aiming to answer two fundamental questions. One, how do retractions influence the scholarly impact of retracted papers, authors, and institutions? Two, does this influence propagate to the wider academic community through scholarly associations? Specifically, we analyzed a set of retracted articles indexed in Thomson Reuters Web of Science (WoS), and ran multiple experiments to compare changes in scholarly impact against a control set of nonretracted articles, authors, and institutions. We further applied the Granger Causality test to investigate whether different scientific topics are dynamically affected by retracted papers occurring within those topics. Our results show two key findings: first, the scholarly impact of retracted papers and authors significantly decreases after retraction, and the most se...

How can spreaders affect the indirect infuence on twitter?

Proceedings of the 12th ACM/IEEE-CS joint conference on Digital Libraries - JCDL '12, 2012

Abstract Most studies on social influence have focused on direct influence, while another interes... more Abstract Most studies on social influence have focused on direct influence, while another interesting question can be raised as whether indirect influence exists between two users who're not directly connected in the network and what affects such influence. In addition, the theory of complex contagion tells us that more spreaders will enhance the indirect influence between two users. Our observation of intensity of indirect influence, propagated by n parallel spreaders and quantified by retweeting probability on Twitter, shows that complex ...

Multiple spreaders affect the indirect influence on twitter

Proceedings of the 21st International Conference on World Wide Web, 2012

Most studies on social influence have focused on direct influence, while another interesting ques... more Most studies on social influence have focused on direct influence, while another interesting question can be raised as whether indirect influence exists between two users who're not directly connected in the network and what affects such influence. In addition, the theory of complex contagion tells us that more spreaders will enhance the indirect influence between two users. Our observation of intensity of indirect influence, propagated by n parallel spreaders and quantified by retweeting probability on Twitter , shows that complex contagion is validated globally but is violated locally. In other words, the retweeting probability increases nonmonotonically with some local drops.

Quantifying socio-economic indicators in developing countries from mobile phone communication data: applications to Côte d’Ivoire

EPJ Data Science, 2015

The widespread adoption of mobile devices that record the communications, social relations, and m... more The widespread adoption of mobile devices that record the communications, social relations, and movements of billions of individuals in great detail presents unique opportunities for the study of social structures and human dynamics at very large scales. This is particularly the case for developing countries where social and economic data can be hard to obtain and is often too sparse for real-time analytics. Here we leverage mobile call log data from Côte d'Ivoire to analyze the relations between its nationwide communications network and the socioeconomic dynamics of its regional economies. We introduce the CallRank indicator to quantify the relative importance of an area on the basis of call records, and show that a region's ratio of in-and outgoing calls can predict its income level. We detect a communication divide between rich and poor regions of Côte d'Ivoire, which corresponds to existing socioeconomic data. Our results demonstrate the potential of mobile communication data to monitor the economic development and social dynamics of low-income developing countries in the absence of extensive econometric and social data. Our work may support efforts to stimulate sustainable economic development and to reduce poverty and inequality.

More spreaders but less influence: An analysis of indirect influence on Twitter

International journal on Semantic Web and information systems

Social influence in social networks has been extensively researched. Most studies have focused on... more Social influence in social networks has been extensively researched. Most studies have focused on direct influence, while another interesting question can be raised as whether indirect influence exists between two users who’re not directly connected in the network and what affects such influence. In addition, the theory of complex contagion tells us that more spreaders will enhance the indirect influence between two users. Our observation of intensity of indirect influence, propagated by n parallel spreaders and quantified by retweeting probability in two Twitter social networks, shows that complex contagion is validated globally but is violated locally. In other words, the retweeting probability increases non-monotonically with some local drops. A quantum cognition based probabilistic model is proposed to account for these local drops.

Comparing the pulses of categorical hot events in Twitter and Weibo

Proceedings of the 25th ACM conference on Hypertext and social media, 2014

The fragility and interconnectivity of the planet argue compellingly for a greater understanding ... more The fragility and interconnectivity of the planet argue compellingly for a greater understanding of how different communities make sense of their world. One of such critical demands relies on comparing the Chinese and the rest of the world (e.g., Americans), where communities' ideological and cultural backgrounds can be significantly different. While traditional studies aim to learn the similarities and differences between these communities via highcost user studies, in this paper we propose a much more efficient method to compare different communities by utilizing social media. Specifically, Weibo and Twitter, the two largest microblogging systems, are employed to represent the target communities, i.e. China and the Western world (mainly United States), respectively. Meanwhile, through the analysis of the Wikipedia pageclick log, we identify a set of categorical 'hot events' for one month in 2012 and search those hot events in Weibo and Twitter corpora along with timestamps via information retrieval methods. We further quantitatively and qualitatively compare users' responses to those events in Twitter and Weibo in terms of three aspects: popularity, temporal dynamic, and information diffusion. The comparative results show that although the popularity ranking of those events are very similar, the patterns of temporal dynamics and information diffusion can be quite different.

Mining topic-level opinion influence in microblog

Proceedings of the 21st ACM international conference on Information and knowledge management, 2012

This paper proposes a Topic-Level Opinion Influence Model (TOIM) that simultaneously incorporates... more This paper proposes a Topic-Level Opinion Influence Model (TOIM) that simultaneously incorporates topic factor and social influence in a two-stage probabilistic framework. Users' historical messages and social interaction records are leveraged by TOIM to construct their historical opinions and neighbors' opinion influence through a statistical learning process, which can be further utilized to predict users' future opinions on some specific topic. We evaluate our TOIM on a large-scaled dataset from Tencent Weibo, one of the largest microblogging website in China. The experimental results show that TIOM can better predict users' opinion than other baseline methods.

The Optimal ROS-Based Symmetric Phase-Only Filter for Fingerprint Verification

2007 IEEE International Conference on Image Processing, 2007

Symmetric phase-only filter (SPOF) has been widely applied to image registration and recognition,... more Symmetric phase-only filter (SPOF) has been widely applied to image registration and recognition, and has been proved efficient for fingerprint verification. Fingerprint images have a characteristic that the dominant information is concentrated in an elliptic frequency band of their ridges in low frequency domain. However, the existent fingerprint verification methods based on SPOF do not take this characteristic into account. To improve the performance of SPOF for fingerprint recognition, an appropriate region of support (ROS) can be used to set the least significant frequency region to zero. By means of theoretical and experimental analysis, we have found the optimal ROS for SPOF that achieves the best discrimination power among all the possible ROSs. Experiments show that our optimal ROS-based SPOF is more efficient than the method of band-limited SPOF (BLPOC).

Topic‐level opinion influence model (TOIM): An investigation using tencent microblogging

Journal of the Association for Information Science and Technology, 2015

Text mining has been widely used in multiple types of user‐generated data to infer user opinion, ... more Text mining has been widely used in multiple types of user‐generated data to infer user opinion, but its application to microblogging is difficult because text messages are short and noisy, providing limited information about user opinion. Given that microblogging users communicate with each other to form a social network, we hypothesize that user opinion is influenced by its neighbors in the network. In this paper, we infer user opinion on a topic by combining two factors: the user's historical opinion about relevant topics and opinion influence from his/her neighbors. We thus build a topic‐level opinion influence model (TOIM) by integrating both topic factor and opinion influence factor into a unified probabilistic model. We evaluate our model in one of the largest microblogging sites in China, Tencent Weibo, and the experiments show that TOIM outperforms baseline methods in opinion inference accuracy. Moreover, incorporating indirect influence further improves inference recal...

A comparative study of academic and Wikipedia ranking

Proceedings of the 13th ACM/IEEE-CS joint conference on Digital libraries, 2013

In addition to its broad popularity Wikipedia is also widely used for scholarly purposes. Many Wi... more In addition to its broad popularity Wikipedia is also widely used for scholarly purposes. Many Wikipedia pages pertain to academic papers, scholars and topics providing a rich ecology for scholarly uses. Scholarly references and mentions on Wikipedia may thus shape the "societal impact" of a certain scholarly communication item, but it is not clear whether they shape actual "academic impact". In this paper we compare the impact of papers, scholars, and topics according to two different measures, namely scholarly citations and Wikipedia mentions. Our results show that academic and Wikipedia impact are positively correlated. Papers, authors, and topics that are mentioned on Wikipedia have higher academic impact than those are not mentioned. Our findings validate the hypothesis that Wikipedia can help assess the impact of scholarly publications and underpin relevance indicators for scholarly retrieval or recommendation systems.

How the Scientific Community Reacts to Newly Submitted Preprints: Article Downloads, Twitter Mentions, and Citations

PLoS ONE, 2012

We analyze the online response to the preprint publication of a cohort of 4,606 scientific articl... more We analyze the online response to the preprint publication of a cohort of 4,606 scientific articles submitted to the preprint database arXiv.org between October 2010 and May 2011. We study three forms of responses to these preprints: downloads on the arXiv.org site, mentions on the social media site Twitter, and early citations in the scholarly record. We perform two analyses. First, we analyze the delay and time span of article downloads and Twitter mentions following submission, to understand the temporal configuration of these reactions and whether one precedes or follows the other. Second, we run regression and correlation tests to investigate the relationship between Twitter mentions, arXiv downloads and article citations. We find that Twitter mentions and arXiv downloads of scholarly articles follow two distinct temporal patterns of activity, with Twitter mentions having shorter delays and narrower time spans than arXiv downloads. We also find that the volume of Twitter mentions is statistically correlated with arXiv downloads and early citations just months after the publication of a preprint, with a possible bias that favors highly mentioned articles.

Improving news ranking by community tweets

Proceedings of the 21st International Conference on World Wide Web, 2012

Users frequently express their information needs by means of short and general queries that are d... more Users frequently express their information needs by means of short and general queries that are difficult for ranking algorithms to interpret correctly. However, users' social contexts can offer important additional information about their information needs which can be leveraged by ranking algorithms to provide augmented, personalized results. Existing methods mostly rely on users' individual behavioral data such as clickstream and log data, but as a result suffer from data sparsity and privacy issues. Here, we propose a Community Tweets Voting Model (CTVM) to re-rank Google and Yahoo news search results on the basis of open, large-scale Twitter community data. Experimental results show that CTVM outperforms baseline rankings from Google and Yahoo for certain online communities. We propose an application scenario of CTVM and provide an agenda for further research.

Adding community and dynamic to topic models

Journal of Informetrics, 2012

The detection of communities in large social networks is receiving increasing attention in a vari... more The detection of communities in large social networks is receiving increasing attention in a variety of research areas. Most existing community detection approaches focus on the topology of social connections (e.g., coauthor, citation, and social conversation) without considering their topic and dynamic features. In this paper, we propose two models to detect communities by considering both topic and dynamic features. First, the Community Topic Model (CTM) can identify communities sharing similar topics. Second, the Dynamic CTM (DCTM) can capture the dynamic features of communities and topics based on the Bernoulli distribution that leverages the temporal continuity between consecutive timestamps. Both models were tested on two datasets: ArnetMiner and Twitter. Experiments show that communities with similar topics can be detected and the co-evolution of communities and topics can be observed by these two models, which allow us to better understand the dynamic features of social networks and make improved personalized recommendations.

Modeling Indirect Influence on Twitter

International Journal on Semantic Web and Information Systems, 2012

Social influence in social networks has been extensively researched. Most studies have focused on... more Social influence in social networks has been extensively researched. Most studies have focused on direct influence, while another interesting question can be raised as whether indirect influence exists between two users who’re not directly connected in the network and what affects such influence. In addition, the theory of complex contagion tells us that more spreaders will enhance the indirect influence between two users. The authors’ observation of intensity of indirect influence, propagated by n parallel spreaders and quantified by retweeting probability in two Twitter social networks, shows that complex contagion is validated globally but is violated locally. In other words, the retweeting probability increases non-monotonically with some local drops. A quantum cognition based probabilistic model is proposed to account for these local drops.

Evolutionary selection of network structure and function

Proc. of the Alife XII Conference, Odense, Denmark, 2010

We explore the relationship between evolved neural network structure and function, by applying gr... more We explore the relationship between evolved neural network structure and function, by applying graph theoretical tools to the analysis of the topology of artificial neural networks known to exhibit evolutionary increases in dynamical neural complexity. Our results suggest a synergistic convergence between network structures emerging due to physical constraints, such as wiring length and brain volume, and optimal network topologies evolved purely for function in the absence of physical constraints. We observe increases in clustering coefficients in concert with decreases in path lengths that together produce a driven evolutionary bias towards smallworld networks relative to comparable networks in a passive null model. These small-world biases are exhibited during the same periods that evolution actively selects for increasing neural complexity (also during which the model's agents are behaviorally adapting to their environment), thus strengthening the association between small-world network structures and complex neural dynamics. We also introduce a new measure of path length in graphs, "normalized path length", that is better behaved than existing metrics for networks comprised of disjoint subgraphs and disconnected nodes, and a novel method of quantifying the degree of evolutionary selection for small world networks, "small-world bias".

Topic-level opinion influence model (TOIM): An investigation using tencent microblogging

Journal of the Association for Information Science and Technology, Jan 30, 2015

Mining user opinion from Micro-Blogging has been extensively studied on the most popular social n... more Mining user opinion from Micro-Blogging has been extensively studied on the most popular social networking sites such as Twitter and Facebook in the U.S., but few studies have been done on Micro-Blogging websites in other countries (e.g. China). In this paper, we analyze the social opinion influence on Tencent, one of the largest Micro-Blogging websites in China, endeavoring to unveil the behavior patterns of Chinese Micro-Blogging users. This paper proposes a Topic-Level Opinion Influence Model (TOIM) that simultaneously incorporates topic factor and social direct influence in a unified probabilistic framework. Based on TOIM, two topic level opinion influence propagation and aggregation algorithms are developed to consider the indirect influence: CP (Conservative Propagation) and NCP (None Conservative Propagation). Users' historical social interaction records are leveraged by TOIM to construct their progressive opinions and neighbors' opinion influence through a statistical learning process, which can be further utilized to predict users' future opinions on some specific topics. To evaluate and test this proposed model, an experiment was designed and a sub-dataset from Tencent Micro-Blogging was used. The experimental results show that TOIM outperforms baseline methods on predicting users' opinion. The applications of CP and NCP have no significant differences and could significantly improve recall and F1-measure of TOIM.