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Posts

Economics of Social Media Fake Accounts

Working paper, presented in CWEIST 2020

The reports of fake social media accounts have caused increasing concerns about the economic and social viability of social media. But the shadow economy around social media fake accounts is still poorly understood, due to the lack of data, transparency, and reliable way of detecting fake accounts. This research uses game-theoretical analysis to understand what makes social media influencers buy fake accounts, how the existence of fake accounts impact consumers, advertisers, social media platforms, and the overall social welfare. The central contribution of this paper is the characterization of equilibrium scenarios. We find that in a pooling equilibrium, only the influencer with low content quality (‘‘low type’’) buys fake accounts while the high type one does not. However, in the ‘‘costly-separating’’ equilibrium, the purchasing behavior flips, i.e., only the influencer with high content quality buys fake accounts while the low type does not. In addition, in the ‘‘costless separating’’ equilibrium, no influencer purchases fake accounts. In terms of the efficiency of the platform-initiated fake accounts detection, we find that the platform could under-detect, over-detect, or efficiently detect the fake accounts. Thus, we may not rely entirely on social media platforms to self-regulate their fake accounts.

Better to Give than to Receive: Impact of Donation Option on Reward-based Crowdfunding Campaigns

Working paper, under 2nd round review at Information Systems Research

Most crowdfunding sites rely on a single funding scheme (e.g., donation, reward, or equity) on their platform, while existing research on backer motivation suggests that backers can be motivated in multiple ways, through intrinsic and extrinsic rewards. Thus, a natural question pertains to whether the use of multiple funding schemes could enhance crowdfunding outcomes. This question critically depends on the interplay between extrinsic and intrinsic motivations. We provide empirical answers to this question by leveraging a natural experiment that took place on a leading reward-based crowdfunding platform that added a new donation option for all reward-based campaigns. Our results indicate that the addition of the donation option to reward-based campaigns has the impact of increasing their success relative to a control group of campaigns. This effect is observed even after accounting for inter-campaign differences via matched samples. To further account for inter-campaign differences, we track campaigns that experienced the site change during the fundraising process, and found that these campaigns gained more funds in the periods after the donation option was introduced. In addition, we find that the heightened crowdfunding success comes from an increase in the number of backers contributing to the campaigns. A finer analysis indicates that the increase in donations has a positive spillover effect on the reward-based contributions, leading to a better crowdfunding outcome. Theoretical and practical implications of our findings are discussed..

Budget Induced Strategic Bidding in Multiunit Online Auctions

Working paper, presented in WITS 2019

Jump bidding is a prevent phenomenon in online auctions with important revenue implications. We propose a novel explanation for jump bidding based on budget constraints – budget-constrained bidders have incentives to jump their bid to increase their likelihood of winning in time-prioritized online auctions. We derive the conditions under which jump bidding is optimal for bidders at the margin, i.e. the current price is one increment away from their maximum bid. We find that the gap between a bidder’s budget and his true valuation and the bid increment jointly influence the likelihood of jump bidding. We propose a hybrid strategic-at-margin (SAM) bidding strategy for budget-constrained bidders. Our discrete-event simulations suggest that SAM outperforms alternative strategies of always bidding the minimum required bid or always jumping. We also propose a proportional SAM bidding strategy that requires less information about other bidders and still outperforms the strategy of bidding the minimum.

Artificial Intelligence (AI) Framework for Venture Capital Industry: How to Predict Startups’ Success in Primary Market

CEO @ DeepSearch.AI, funded by Angel round investment, ongoing

Asymmetry widely appears in venture capital industry including information, knowledge, relationship et al, which heavily affects investors’ decision making. Our startup focus on data-driven venture capital revolution, aiming to empowering investors to discover unicorns. Besides, our technical team supports various data scraping demands. If you need some data to do research or other things, feel free to contact me. We can help you!

Crowdfunding (reward-based and equity-based)

Work at Zhongchou Inc. & Yuanshihui Inc., finished in December 2016

I spent one and a half years in crowdfunding industry, which are two platform experiences including reward-based and equity-based. I was involved as a software engineer at beginning, and then changed to a prodcut manager. My work mainly lied on product design, research and development. Besides, I also kept eyes on trends in industry. At the end of 2016, a paper about crowdfunding market which I coauthored was published in Financial Innovation.

Home Away From Home

Course Project, finished in December 2014, CS, University of Minnesota

This project is to help users better do city exploration with respect to what they are familiar with. In this project I go through the complete user experience design procedure: user research, prototype, cognitive walkthrough, heuristic evaluation, implementation.

Stock Market Prediction based on Neural Networks

Empirical Finance & Financial Econometrics Workshop, finished in November 2011, CCER, Peking University

In this project, I proposed to use neural networks model to predict stock market trends, the performance of prediction is compared to other prediction models such as GARCH model

Power Amplifier Module with High Efficiency, Linearity and Wide Bandwidth

National Key Technology Project, finished in September 2012, EE, Tsinghua University

Due to high nonlinearity and memory effects of dual-band power amplifiers, I did behavioral modeling for concurrent dual-band power amplifier and digital pre-distortion based on 2D-Real Value Time Delayed Neural Networks, achieving an 10dB improvement in model accuracy than conventional approach.

Measurement System of Radiated Emission Based on Current Probe

Supported by Mitsubishi Heavy Industries, finished in May 2010, EE, Tsinghua University

Considering the complex process and high cost of radiation emission measurement in anechoic chamber, I developed a measurement system based on a current probe in common laboratory, reducing the cost of test dramatically while measurement accuracy is maintained.

portfolio

publications

Development of low cost radiated emission measurement system

Published in Microwave and Millimeter Wave Technology (ICMMT), International Conference on, 2010

A novel low cost radiated emission measurement system by using current probe has been proposed in this paper. Compared with conventional anechoic chamber measurement method, this new method could reduce test cost and save developing time efficiently. In order to improve the accuracy, a transfer function has been built for mapping the relationship between current probe method and anechoic chamber method. By using the interpolation, the final estimated radiation emission has been validated and agreed well with the experimental results in anechoic chamber.

Recommended citation: Huang, Zihong, Wenhua Chen, Zhenghe Feng, K. Toyama, and K. Teshima. "Development of low cost radiated emission measurement system." In Microwave and Millimeter Wave Technology (ICMMT), 2010 International Conference on, pp. 1829-1832. IEEE, 2010. [Download paper here]

Development of low cost measurement system for radiated emission evaluation

Published in Progress In Electromagnetics Research Letters, 2011

In this paper, a low cost measurement system with high accuracy for radiated emission evaluation has been proposed. By combining the test data of the current probe at different positions on the harness, the measurement accuracy is improved compared with conventional single probe method. For the sake of high accuracy, a transfer function is built to map the relationship between anechoic chamber method and current probe method. Based on experiments for evaluation, the final estimation of radiated emission agrees well with the measured results in anechoic chamber. For the cases tested, the difference between the current probe method and the anechoic chamber method is less than 3 dB.

Recommended citation: Huang, Zihong, Wenhua Chen, Zhenghe Feng, Kazunori Teshima, and Koji Toyama. "Development of low cost measurement system for radiated emission evaluation." Progress In Electromagnetics Research Letters 20 (2011): 55-68. [Download paper here]

Forward behavioral modeling of concurrent dual-band power amplifiers using extended real valued time delay neural networks

Published in Microwave and Millimeter Wave Technology (ICMMT), International Conference on, 2012

The distortions induced by inter-modulations and cross-modulations in concurrent dual-band power amplifiers (PAs) are evidenced and characterized using multi-tone signals. Given the presence of cross-modulations, a comprehensive extended real-valued time-delay neural network (extended-RVTDNN) is proposed to model the nonlinear behavior of concurrent dual-band Pas. Two three-carrier WCDMA signals are applied to a dual-band Doherty PA prototype for modeling verification. The experimental results show that the proposed model approximates the PA with normalized mean square errors (NMSEs) of -38.48dB and -35.42dB in the lower and upper bands, respectively. Compared with the conventional single-band RVTDNN, this new method achieves an improvement in accuracy of more than 10dB.

Recommended citation: Huang, Zihong, Wenhua Chen, Zhenghe Feng, and Fadhel M. Ghannouchi. "Forward behavioral modeling of concurrent dual-band power amplifiers using extended real valued time delay neural networks." In Microwave and Millimeter Wave Technology (ICMMT), 2012 International Conference on, vol. 5, pp. 1-4. IEEE, 2012. [Download paper here]

Precision CrowdSourcing: Closing the Loop to Turn Information Consumers into Information Contributors

Published in Proceedings of the 19th ACM Conference on Computer-Supported Cooperative Work & Social Computing (CSCW), 2016

We introduce a theoretical framework called precision crowdsourcing whose goal is to help turn online information consumers into information contributors. The framework looks at the timing and nature of the requests made of users and the feedback provided to users with the goal of increasing long-term contribution and engagement in the site or system. We present the results of a field experiment in which almost 3000 users were asked to tag movies (plus a null control group) as we varied the selection of task (popular/obscure), timing of requests (immediate or varying delays), and relational rhetoric (neutral, system reciprocal, other users reciprocal) of the requests. We found that asking increases tags provided overall, though asking generally decreases the provision of unprompted tags. Users were more likely to comply with our request when we asked them to tag obscure movies and when we used reciprocal request rhetoric.

Recommended citation: Zhao, Qian, Zihong Huang, F. Maxwell F. Harper, Loren G. Terveen, and Joseph A. Konstan. "Precision CrowdSourcing: Closing the Loop to Turn Information Consumers into Information Contributors." In Proceedings of the 19th ACM Conference on Computer-Supported Cooperative Work & Social Computing, pp. 1613-1623. ACM, 2016. [Download paper here]

Pure and Hybrid Crowds in Crowdfunding Markets

Published in Financial Innovation, 2016

This study documents and compares two crowd designs for crowdfunding, namely pure crowds, where all crowd members participate as equals, and hybrid crowds, where crowd members are led by an expert investor. The hybrid design is rarely studied in the crowdfunding literature despite its large presence in equity crowdfunding. We examine industry practices from various countries in terms of crowd designs, review relevant literature on this topic, and develop a conceptual framework for choosing between pure and hybrid crowds. We identify several inefficiencies of pure crowds in crowdfunding platforms and discuss the advantages of hybrid crowds. We then develop a conceptual framework that illustrates the factors for choosing between pure and hybrid crowds. Finally, we discuss the issue of how to manage and regulate lead investors in hybrid crowds. Our study contributes to the crowdfunding literature and to crowdfunding practice in multiple ways.

Recommended citation: Chen, Liang, Zihong Huang, and De Liu. "Pure and hybrid crowds in crowdfunding markets." Financial Innovation 2.1 (2016): 19. [Download paper here]

talks

teaching

Descriptive and Predictive Analytics (Fall 2018, Spring 2020)

IDSC 4444, Carlson School of Management, University of Minnesota, 2018

In a world of ever growing information sources, any student of business should be equipped with the ability to analyze data to produce actionable insights. Equally important is the capacity to understand such analysis and to present it to key stakeholders. IDSc 4444 offers an introduction to basics of data manipulation, visualization and analysis for business intelligence.

Information Technologies and Solutions (Fall 2018)

IDSC 6050, Carlson School of Management, University of Minnesota, 2018

This course is about current/emerging technologies that are used in modern net-enhanced organizations. Topics covered will include mobile communications, information security, cloud computing, blockchains, and emerging IT trends.

Exploratory Data Analytics and Visualization (Fall 2019)

MSBA 6410, Carlson School of Management, University of Minnesota, 2019

MSBA 6410 is a required course for the M.S. in Business Analytics program. This course is designed to prepare aspiring data scientists for the rapidly changing digital environment faced by companies and their need to discover novel and actionable patterns, enabling data-driven decision making.

Interactive Data Visualization for Business Analytics (Spring 2020)

IDSC 4210, Carlson School of Management, University of Minnesota, 2020

IDSC 4210 is an elective course for the undergraduate Business Analytics minor at the Carlson School of Management. It focuses on the fundamental and widely used exploratory data analysis technique of interactive visualization that is integral to modern business analytics.