How Can Our Tweets Go Viral? Point-Process Modelling of Brand Content

2022 | Information & Management | Citations: 0

Authors: Zadeh, Amir; Sharda, Ramesh

Abstract: People create and share content via online social networks, which provide an unp ... Expand

Abstract: People create and share content via online social networks, which provide an unparalleled opportunity for brands to gain visibility, promote products or services and drive revenue growth. Much research has focused on why, how, or what social content is popular, trending and “hype”. One central challenge is to forecast the spread (cascades) of information that leads to the popularity of content throughout a social network. Online content tends to have bursts and spikes, experiencing a different cascading pattern depending on the viral propagation. In this paper, we propose and test a flexible framework capable of modelling such patterns and trends. We take temporal and network perspectives and develop a model based on the multivariate Hawkes processes that ac­ count for social behaviour and network elements such as follower counts, and activity variation observed in collective re-sharing behaviour. We focus on Twitter as the most widely used micro-blogging online social network and measure the popularity of a brand’s tweet by analysing the time-series path of three types of subsequent activities (retweets (RTs), replies (REs) and likes (LKs)). The specific model that we propose in this paper is the multidimensional epidemic-type aftershock sequence (METAS) model, a particular case of the multivariate Hawkes process. It consists of a power-law relaxation governing the timing of activities. It also includes an exponential boost as a reinforcement mechanism for the response amplitude to model the impact of influential users on their followers. Earlier attempts to model online cascades have treated all online responses as one type of activity. Rather than aggregating all the activities into one stream, and therefore, ignoring exciting effects among different types of activities, we incorporate the activity variation into the predictive models of content popularity, explicitly accounting for such excitation effects. We develop epidemic-type mutually exciting Hawkes point processes models to quantify such effects and to predict more accurately the number of follow-up activities (i.e., RTs, REs and LKs) on a brand tweet after it is posted. Our results suggest that the proposed model outperforms the state-of-the-art models in terms of prediction accuracy, as it is able to account for mutual ex­ citations and cross-interactions between sequences of users’ activities from one type to another. These results are relevant for developing and executing a plan for online activities by the brand owners. Collapse

Topics: Twitter social media social network pandemic marketing management

Methods: business process modeling longitudinal research time series analysis parametric test mathematical model

Online video channel management: An integrative decision support system framework

2021 | International Journal of Information Management | Citations: 0

Authors: France, Stephen L.; Shi, Yuying; Vaghefi, Mahyar Sharif; Zhao, Huimin

Abstract: In the current fragmented media landscape, online video is becoming an important ... Expand

Abstract: In the current fragmented media landscape, online video is becoming an important outlet for content dissemi­ nation. Online video channels provide content creators a way of organizing content and building an online following through subscriptions and social sharing. This paper describes a decision support systems (DSS) based framework for online video channel management and content creation. The relevant DSS literature was reviewed along with both modeling and behavioral aspects of online videos and video channels. An empirical case study was run on a dataset consisting of views, shares, and subscriptions from over 1000 videos from nine YouTube channels. This paper contributes to DSS theory by proposing a flexible framework for incorporating both behavioral and empirical work into an integrative process for online video content creation. This framework builds on existing data-driven DSS theory, but includes specific entities and processes for online content creation. Collapse

Topics: decision support system logistics management YouTube advertising management marketing management

Methods: experiment case study autoregressive integrated moving average literature study longitudinal research

Theories: behavioral theory

Online to Offline: The Impact of Social Media on Offline Sales in the Automobile Industry

2021 | Information Systems Research | Citations: 16

Authors: Wang, Yen-Yao; Guo, Chenhui; Susarla, Anjana; Sambamurthy, Vallabh

Abstract: Given the limited research into the impact of social media on offline sales of d ... Expand

Abstract: Given the limited research into the impact of social media on offline sales of durable goods, this study examines the dynamic relationships between firm-generated content (FGC), user-generated content (UGC), traditional media, and offline light vehicle sales. Data were collected from the official Facebook and Twitter pages of 30 U.S. car brands from 2009 to 2015. We utilized a panel vector autoregressive model to investigate the dynamic relationships among multiple time series variables while controlling for influential durable goods characteristics. The empirical results suggest that Facebook and Twitter have heterogeneous effects on offline vehicle sales. Moreover, FGC is more effective than UGC for influencing offline light vehicle sales. Viral impressions from Facebook and Twitter are essential, although the effects vary by social media platform (Facebook versus Twitter) and content type (FGC versus UGC). The result of the impulse response function analysis indicates that both the firm’s marketing efforts (FGC and traditional media) and UGC have a long-term effect on offline sales, with the long-term effect of a firm’s marketing efforts outlasting that of UGC. Incorporating FGC and UGC from Facebook and Twitter and traditional media could improve the performance of offline sales prediction. We also documented the within-Twitter synergistic effect between FGC and UGC for offline car sales and cross-channel substitution relationships (FGC on both Facebook and Twitter serves as a substitution to traditional media). Finally, we provide guidance for managers seeking to leverage multichannel marketing to boost the offline sales of durable goods. Collapse

Topics: Twitter Facebook social media marketing management database system

Methods: longitudinal research Granger causality Monte Carlo simulation time series analysis cross sectional research

The JDownloader Immune System for Continuous Deployment

2020 | HICSS | Citations: 0

Authors: Rechenmacher, Thomas; Riehle, Dirk; Weber, Michael

Abstract: Continuous deployment can reduce the time from a source code change to a newly d ... Expand

Abstract: Continuous deployment can reduce the time from a source code change to a newly deployed application significantly. Increased innovation speed can make all the difference in a competitive market situation. However, deploying at high frequency requires high speeds of discovering bugs in the deployed software. Using the JDownloader file download manager as our example, we present a fitness model to evaluate a continuously deployed software during operation for expected behavior, present the design and implementation of a monitoring component, and evaluate the model and its implementation using data from JDownloader’s multimillion member strong user base. Our evaluation finds that there had been thousands of undetected bugs, and that newly created bugs can be detected and reported 16 times faster than before. Collapse

Topics: privacy open source source code open source software project

Methods: longitudinal research computational algorithm time series analysis mathematical model moving average model

The Interaction Between Microblog Sentiment and Stock Returns: An Empirical Examination

2018 | Management Information Systems Quarterly | Citations: 4

Authors: Deng, Shuyuan; Huang, Zhijian (James); Sinha, Atish P.; Huimin Zhao

Abstract: Opinion mining of microblog messages has become a popular application of busines ... Expand

Abstract: Opinion mining of microblog messages has become a popular application of business analytics in recent times. Opinions reflected in microblogs have provided businesses with great opportunities to acquire insights into their operating environments in real time. In particular, the relationship between microblog sentiment and stock returns is of great interest to investment professionals and academic researchers across multiple disciplines. We empirically test this complex relationship in a comprehensive study. We perform vector autoregression on a data set containing close to 18 million microblog messages spanning 4 years at the market and the individual stock levels, and at the daily and the hourly frequencies. The results show that the influence of microblog sentiment on stock returns is both statistically and economically significant at the hour level. Microblog sentiment is also largely driven by movements in the market. Moreover, stock returns have a stronger influence on negative sentiment than on positive sentiment. These findings have important implications for both research and practice. Collapse

Topics: microblog Twitter blog big data social media analytics

Methods: longitudinal research Granger causality sentiment analysis machine learning vector autoregression

How Does Social Media Impact Bitcoin Value? A Test of the Silent Majority Hypothesis

2018 | Journal of Management Information Systems | Citations: 87

Authors: Mai, Feng; Shan, Zhe; Bai, Qing; Wang, Xin (Shane); Chiang, Roger H.L.

Abstract: Bitcoin’s emergence has the potential to pave the way for a technological revolu ... Expand

Abstract: Bitcoin’s emergence has the potential to pave the way for a technological revolution in financial markets. What determines its valuation is an important open question with far-reaching business and policy implications. Building on information systems and finance literature, we examine the dynamic interactions between social media and the monetary value of bitcoin using textual analysis and vector error correction models. We show that more bullish forum posts are associated with higher future bitcoin values. Interestingly, social media’s effects on bitcoin are driven primarily by the silent majority, the 95 percent of users who are less active and whose contributions amount to less than 40 percent of total messages. In addition, messages on an Internet forum, relative to tweets, have a stronger impact on future bitcoin value. Overall, our findings reveal that social media sentiment is an important predictor in determining bitcoin’s valuation, but not all social media messages are of equal impact. This study offers new insights into the digital currency market and the economic impact of social media. Collapse

Topics: Bitcoin social media Twitter web forum internet technology

Methods: forums posting longitudinal research Granger causality moving average model augmented Dickey–Fuller test

Theories: information systems theory

The Case for a Simple Two-Sided Electricity Market

2017 | HICSS | Citations: 0

Authors: Lamadrid, Alberto; Jeon, Wooyoung; Lu, Hao; Mount, Tim

Abstract: This paper builds on the results from our earlier research on the design of elec ... Expand

Abstract: This paper builds on the results from our earlier research on the design of electricity markets that have to accommodate the uncertainty associated with high penetrations of renewable sources of energy. The key results show that 1) distributed storage (deferrable demand) is an effective way to reduce total system costs, 2) a simple market structure for energy allows aggregators to meet their customers’ energy needs and provide ramping services to the system operator, and 3) using a receding-horizon optimization to dispatch units for the next market time-step benefits from the availability of more accurate forecasts of renewable generation and allows market participants to adjust their bids and offers in response to this new information. In our two-sided market, distributed storage in the form of deferrable demand is controlled locally by independent aggregators to minimize their expected payments for energy in the wholesale market, subject to meeting the energy needs of their customers. In addition, these aggregators are responsible for maintaining a stable power factor by installing local capabilities that automatically deal with local power imbalances. Failure to do this triggers penalties paid to the system operator. Collapse

Topics: distributed storage IT investment information system use

Methods: longitudinal research moving average model econometric model time series analysis

Explaining U.S. consumer behavior with news sentiment

2011 | ACM Transactions on Management Information Systems | Citations: 0

Authors: Uhl, Matthias W.

Abstract: We introduce a novel dataset with a news sentiment index that was constructed fr ... Expand

Abstract: We introduce a novel dataset with a news sentiment index that was constructed from a selection of over 300,000 newspaper articles from five of the top ten U.S. newspapers by circulation. By constructing ARMA models, we show that news and consumer sentiment, when combined with other macroeconomic variables, achieve statistically significant results to explain changes in private consumption. We make three distinct findings with respect to sentiment in consumption behavior models: first, both consumer and news sentiment add explanatory power and statistical significance to conventional consumer behavior models. Second, consumer sentiment, measured by the University of Michigan Index of Consumer Sentiment, adds more explanatory power and statistical significance than news sentiment when tested individually. Third, news sentiment is able to determine the signs of all coefficients in the model correctly, whereas consumer sentiment does not. In general, we conclude that news sentiment is a useful variable to add in consumer behavior models, especially when coupled with consumer sentiment and other macroeconomic variables. Tested individually, news sentiment is as good a proxy as personal income for explaining private consumption growth when tested individually. Collapse

Topics: consumer behavior database system Java website electronic mail

Methods: computational algorithm longitudinal research autoregressive moving average model autocorrelation analysis design science

THE VALUE OF ONLINE PRODUCT BUZZ IN SALES FORECASTING

2010 | International Conference on Information Systems | Citations: 0

Authors: Reijden, Peter v.d.; Koppius, Otto

Abstract: Online product buzz refers to an online expression of interest in a product, suc ... Expand

Abstract: Online product buzz refers to an online expression of interest in a product, such as online product reviews, blog posts and search trends. We study how well online buzz predicts actual sales across different phases in the product lifecycle. Using data from smartphone sales of a Dutch online retailer, we demonstrate a 28% overall increase in forecasting accuracy when measures of online product buzz variables are take into account. The value of online product buzz shows especially in early sales forecasting, an area in which traditional forecasting models have substantial difficulties. In early sales, local search trends, subscriptions for stock notifications and pageviews were important predictors and forecasts were on average improved by 44%. For mature sales, accuracy improved by 10%, with the most important predictors being on- and offsite reviews and, again, pageviews. These results also suggest different drivers of sales across phases in the product lifecycle. Collapse

Topics: smartphone Google+ online review innovation management organizational value

Methods: random forest classification machine learning descriptive statistic longitudinal research moving average model

Short-term prediction models for server management in Internet-based contexts

2009 | Decision Support Systems | Citations: 0

Authors: Casolari, Sara; Colajanni, Michele

Abstract: Modern Internet applications run on top of complex system infrastructures where ... Expand

Abstract: Modern Internet applications run on top of complex system infrastructures where several runtime management algorithms have to guarantee high performance, scalability and availability. This paper aims to offer a support to runtime algorithms that must take decisions on the basis of historical and predicted load conditions of the internal system resources. We propose a new class of moving filtering techniques and of adaptive prediction models that are specifically designed to deal with runtime and short-term forecast of time series which originate from monitors of system resources of Internet-based servers. A large set of experiments confirm that the proposed models improve the prediction accuracy with respect to existing algorithms and they show stable results for different workload scenarios. Collapse

Topics: internet technology database system website MySQL usability

Methods: longitudinal research computational algorithm autoregressive integrated moving average experiment time series analysis