From Opaque to Accountable Governance: Investor Activism for Transparency in Social Media Amid Disruption in Cryptocurrency

2023 | International Conference on Information Systems | Citations: 0

Authors: Myeong, Jaeho; Jung, Miyeon; Auh, Seigyoung; Han, Sang Pil

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Topics: Twitter social media cryptocurrency blockchain Bitcoin

Methods: proportional hazards model survival analysis mediation analysis longitudinal research econometric modeling

Theories: uncertainty reduction theory

Understanding Behavioral Drivers in Twitter Social Media Networks

2023 | International Conference on Information Systems | Citations: 0

Authors: Chung, Wingyan

Abstract: As social media platforms facilitate user interactions, organizations increasin ... Expand

Abstract: As social media platforms facilitate user interactions, organizations increasingly use social media networks (SMNs) to build network ties. Studying user behavior on SMNs can help to uncover strategic information and improve situation awareness. However, there is a lack of understanding of behavioral drivers of SMN participants. This research developed a theoretically-based IS development framework for modeling user behavior in large evolving SMNs. To demonstrate the feasibility of our framework, we developed a proof-of-concept system for simulating user activities in the SMNs of Twitter social communities. Our system models the complex behavioral features in the SMNs by using a wide range of theoretically-driven features and machine-discovered features, and predicts user activities by using a pipeline of statistical and machine-learning techniques. Preliminary results of a simulation study provide insights of the importance of comprehensive network features to model SMN group behavior accurately and quality of commitment features to model SMN user behavior. Collapse

Topics: Twitter user behavior systems development YouTube decision making

Methods: simulation machine learning support vector machine design artifact mixed method

Theories: social network theory

The Good, the Bad, and the Social Media: Financial Implications of Social Media Reactions to Firm-Related News

2022 | Journal of Management Information Systems | Citations: 0

Authors: Peng, Jing; Zhang, Juheng; Gopal, Ram

Abstract: Firms and investors often react to financial news on social media. However, how ... Expand

Abstract: Firms and investors often react to financial news on social media. However, how they react to news of different nature and whether their reactions influence the stock market is far from clear. Employing data on financial news, tweets posted by firms and investors, and daily stock prices, we find that firms are more responsive to news with positive sentiment and low uncertainty, whereas investors are more responsive to news with high uncertainty. Moreover, the increased tweeting activities of firms and investors can improve the stock returns of firms. We further show that investors' social media reactions to news and the subsequent influence on stock returns depend on firm size. This paper provides a fuller picture of how firms, investors, and the stock market react to financial news, and reveals the nuanced interactions among them. We discuss how firms and investors can better leverage social media to improve stock performance. Collapse

Topics: Twitter social media financial performance missing data accounting

Methods: event study robustness check longitudinal research Granger causality cross sectional research

Popular News Are Relevant News! How Investor Attention Affects Algorithmic Decision-Making and Decision Support in Financial Markets

2021 | Information Systems Frontiers | Citations: 0

Authors: Clapham, Benjamin; Siering, Michael; Gomber, Peter

Abstract: Algorithmic decision-making plays an important role in financial markets. Curren ... Expand

Abstract: Algorithmic decision-making plays an important role in financial markets. Current tools in trading focus on popular companies which are discussed in thousands of news items. However, it remains unclear whether methodologies from the field of data analytics relying on large samples can also be applied to small datasets of less popular companies or whether these methodologies lead to the discovery of meaningless patterns resulting in economic losses. We analyze whether the impact of media sentiment on financial markets is influenced by two levels of investor attention and whether this impacts algorithmic decision-making. We find that the influence differs substantially between news and companies with high and low investor attention. We apply a trading simulation to outline the practical consequences of these interrelations for decision support systems. Our results are of high importance for financial market participants, especially for algorithmic traders that consider sentiment for investment decision support. Collapse

Topics: social media decision support system website digital media decision making

Methods: simulation sentiment analysis sensitivity analysis descriptive statistic regression analysis method

Theories: behavioral economics cognitive attention theory transaction cost economics

The Voice of the Customer: Managing Customer Care in Twitter

2020 | Information Systems Research | Citations: 6

Authors: Mousavi, Reza; Johar, Monica; Mookerjee, Vijay S.

Abstract: In recent years, managing customer sentiment—particularly on social media—has be ... Expand

Abstract: In recent years, managing customer sentiment—particularly on social media—has become crucial as more customers use social media to seek help from firms. Therefore, we strive to determine an optimal strategy to manage customer sentiment on social media sites such as Twitter. We also aim to identify factors and external events that can influence the effectiveness of customer care. To understand the antecedents of digital customer care, we model a diffusion process of customer sentiment over time. This diffusion process is influenced (or controlled) by the firm through the strategy employed to respond to customer tweets. We then use real data consisting of sentiments expressed by customers directed at Twitter’s service accounts of four major U.S. telecommunication-service providers (AT&T, Verizon, Sprint, and T-Mobile) to estimate the parameters in our analytical model and shed several insights into digital customer care in this industry. First, we find a clear separation among the firms in terms of digital customer care effectiveness. Second, we find that good customer care is not merely a matter of responding to customer tweets. Third, the quality of digital customer care that customers expect varies across firms: Customers of higher priced firms (e.g., Verizon and AT&T) expect better customer care. Fourth, seemingly unrelated events (such as signing an exclusive contract with a celebrity) can impact digital customer care. Our study has important implications for managers as it can help firms determine the optimal strategy to influence customer sentiment. Collapse

Topics: Twitter customer service application programming interface social media Apple iPhone

Methods: sentiment analysis longitudinal research stochastic model simulation generalized autoregressive conditional heteroskedasticity model

Don’t Mention It? Analyzing User-Generated Content Signals for Early Adverse Event Warnings

2019 | Information Systems Research | Citations: 0

Authors: Abbasi, Ahmed; Li, Jingjing; Adjeroh, Donald; Abate, Marie; Zheng, Wanhong

Abstract: With greater impetus on broad postmarket surveillance, the Voice of the Customer ... Expand

Abstract: With greater impetus on broad postmarket surveillance, the Voice of the Customer (VoC) has emerged as an important source of information for understanding consumer experiences and identifying potential issues. In organizations, risk management groups are increasingly interested in working with their information technology teams to develop robust VoC listening platforms. Two key challenges have impeded success. First, prior work has leveraged diverse sets of channels, adverse event types, and modeling methods, resulting in diverging conclusions regarding the viability and efficacy of various user-generated channels and accompanying modeling methods. Second, many existing detection methods rely on “mention models” that have low detection rates, have high false positives, and lack timeliness. Following the information systems design science approach, in this research note we propose a framework for examining key design elements for VoC listening platforms. As part of our framework, we also develop a novel heuristic-based method for detecting adverse events. We evaluate our framework and method on two large test beds each encompassing millions of tweets, forums postings, and search query logs pertaining to hundreds of adverse events related to the pharmaceutical and automotive industries. The results shed light on the interplay between user-generated channels and event types, as well as the potential for more robust event modeling methods that go beyond basic mention models. Our analysis framework reveals that user-generated content channels can facilitate timelier detection of adverse events: on average, two to three years or earlier than commonly used databases. The inclusion of negative sentiment polarity in the models can further reduce false-positive rates. Additionally, we find social media channels provide higher detection rates but lower precision than do search-based signals. The search and web forum channels are timelier than Twitter. The proposed heuristic-based method attains markedly better results than do existing methods—with earlier detection rates of 50%–80% and far fewer false positives across an array of VoC channels and event types. The heuristic method is also well suited for signal fusion across channels. Our note makes several contributions to research. The results also have important implications for various practitioner groups, including regulatory agencies and risk management teams at product manufacturing firms. Collapse

Topics: Twitter social media user-generated content database system digital platform

Methods: machine learning time series analysis longitudinal research design science parametric test

HOW DO LARGE STAKES INFLUENCE BITCOIN PERFORMANCE? EVIDENCE FROM THE MT.GOX LIQUIDATION CASE

2019 | European Conference On Information Systems | Citations: 0

Authors: Kremser, Thomas; Radszuwill, Sven

Abstract: Bitcoin as the first and still most important decentralized cryptocurrency has g ... Expand

Abstract: Bitcoin as the first and still most important decentralized cryptocurrency has gained wide popularity due to the steep rise of its price during the second half of 2017. Because of its digital nature, Bitcoin cannot be valuated exclusively with fundamental approaches, which is why factors such as investor sentiment have become a common alternative to capture its performance. In this work, we studied whether and how the sale of Bitcoins from the insolvency assets of Mt.Gox, which represent about 1.1% of the current global total, relates to Bitcoin price movements. We used social media sentiment analysis of Twitter data to examine how investors are influenced in their decision to buy or sell Bitcoin when confronted with the trade actions of Nobuaki Kobayashi, the trustee in charge of the Mt.Gox case. We built a vector error correction model to analyse the long-run relationship between cointegrated variables. Our analysis confirms the positive association of Bitcoin performance with positive Twitter sentiment and tweet volume and the negative association with negative sentiment. We further found empirical evidence that Mt.Gox selloff events have a lasting negative impact on the Bitcoin price and that we can measure this effect by Twitter sentiment and tweet volume. Collapse

Topics: Bitcoin Twitter cryptocurrency social media user-generated content

Methods: longitudinal research time series analysis event study sentiment analysis parametric test

Should firms invest in social commerce? An integrative perspective

2019 | Information & Management | Citations: 1

Authors: Lam, Hugo K. S.; Yeung, Andy C. L.; Lo, Chris K. Y.; Cheng, T. C. E.

Abstract: Integrating resource-based view and opportunity–motivation–ability framework, we ... Expand

Abstract: Integrating resource-based view and opportunity–motivation–ability framework, we theorize that social commerce represents firms’ opportunity to access valuable social media resources and gain a competitive advantage. We further argue that product uncertainty and firm reputation indicate firms’ motivation and ability, respectively, to use the social media resources, affecting the extent to which the competitive advantage can be realized. An event study of 275 social commerce initiatives announced between 2006 and 2011 supports our arguments, showing that social commerce increases firms’ stock returns, but such increases are more significant for firms selling products with high uncertainty and having high reputation. Collapse

Topics: social commerce social media competitive advantage organizational productivity Facebook

Methods: event study longitudinal research nonparametric test cross sectional research regression analysis method

Theories: resource based view of the firm

Information or noise: How Twitter facilitates stock market information aggregation

2019 | International Conference on Information Systems | Citations: 0

Authors: Pöppe, Thomas; Kiesel, Florian; Kolaric, Sascha; Schiereck, Dirk

Abstract: We assess the relevance of Twitter for stock-relevant information dissemination ... Expand

Abstract: We assess the relevance of Twitter for stock-relevant information dissemination in financial markets on the single stock level. We use a unique dataset including more than 12 million Twitter feeds linked to specific firms. Using intraday data for the computation of advanced trading metrics, such as effective spreads, intraday volatility, and a daily version of the microstructure variable probability of informed trading (PIN), we measure the impact of Twitter activity on trading and information dissemination. The PIN model indicates that more uninformed than informed traders rush to the market along with rising Twitter activity. These results indicate that Twitter serves as an excellent indicator of news that is relevant for the stock market. However, we show that Twitter does not lead traditional news channels. In contrast, Twitter activity follows the market and has no predictive power with regard to future stock trading volume or volatility on the single stock level. Collapse

Topics: Twitter web forum microblog

Methods: longitudinal research descriptive statistic event study nonparametric test Kruskal Wallis test

The economics of stock touting during Internet-based pump and dump campaigns

2019 | Information Systems Journal | Citations: 0

Authors: Siering, Michael

Abstract: Information systems have facilitated the increase in relevance of financial mark ... Expand

Abstract: Information systems have facilitated the increase in relevance of financial markets. Nevertheless, the rise of the Internet has eased information‐based financial market manipulations. In this study, we examine the phenomenon of stock touting during pump and dump campaigns, in which deceivers advertise stocks to profit from an increased price level. We observe that the positive prospects promised are not confirmed by corporate disclosures and financial news. Furthermore, manipulators select targeted financial instruments based on specific stock and company characteristics. Manipulators avoid signals of anomaly and prefer unknown stocks. We find that stock touting has a positive market impact but that it is followed by a large decline in stock price in the subsequent days, causing investors to lose substantial amounts of their investments. We consider the impact of information generation, information content, and information presentation on the corresponding market reaction. Interestingly, information generation influences the demand for the stock, but information content and information presentation drive the willingness to pay. Our results are highly relevant for Internet users, software vendors, and market surveillance authorities, as a deep understanding of such information‐based manipulations is necessary to develop appropriate countermeasures. Collapse

Topics: internet technology deception detection willingness to pay advertising management email spam

Methods: longitudinal research event study sentiment analysis nonparametric test Wilcoxon signed-rank test