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

Impact of Incentive Mechanism in Online Referral Programs: Evidence from Randomized Field Experiments

2021 | Journal of Management Information Systems | Citations: 8

Authors: Jung, Jaehwuen; Bapna, Ravi; Gupta, Alok; Sen, Soumya

Abstract: Despite the growing popularity of online referral programs, a minimal amount is ... Expand

Abstract: Despite the growing popularity of online referral programs, a minimal amount is known regarding the theoretical foundations that drive the key actions associated with successful referrals. In this paper, we study which type of referral reward structure is most effective in maximizing word-of-mouth by conducting two randomized experiments in mobile gaming context. Specifically, we examine the effect of three incentive schemes: selfish reward (inviter gets all the reward), equal-split reward (50-50 split), and generous reward (invitee gets all the reward). Consistent across the two experiments, we find that pro-social referral incentive schemes, namely the equal-split and generous schemes, tend to dominate purely selfish schemes in creating WOM. Our mechanism-level analysis shows that both equal-split and generous schemes result in higher number of conversions by significantly increasing the invitee's likelihood to accept referrals, which we further show that is partially due to selective and better targeted referrals. Our results contribute to the understanding of the optimal design of online referral programs and provide important implications for designing effective referral reward schemes in the digital world. Collapse

Topics: mobile application mobile system mobile game word of mouth online word of mouth

Methods: experiment experimental group randomized field experiment field experiment Poisson regression

Theories: social contagion theory

Seeding for Complementary Campaign Objectives in Social Networks

2021 | Americas Conference on Information Systems | Citations: 0

Authors: Karczmarczyk, Artur; Wątróbski, Jarosław; Jankowski, Jaroslaw

Abstract: Various theoretical models are used in research into the dissemination of infor ... Expand

Abstract: Various theoretical models are used in research into the dissemination of information in social networks. The assumed goals include selecting seeds in order to maximize the influence or reach the target subset of the network users (nodes). On the other hand, the multi-attribute nature of individual network nodes indicates the possibility of analyzing their multi-criteria nature, as well as the consequent use of MCDA methods in the process of seed selection. The state of art shows, however, that this contribution is still missing. This paper presents an attempt to use the multi-criteria decision analysis (MCDA) in the seed selection process and thus a methodological framework supporting reaching many varied sets of target nodes. As a result, a single viral marketing campaign in a social network can be performed to target multiple (often separate) sets of target nodes, thus fulfilling the objectives normally achieved with multiple campaigns. Collapse

Topics: viral marketing marketing management social network

Methods: simulation trade-off analysis method TOPSIS analytic hierarchy process multiple criteria decision making

Cross-Platform Spillover Effects in Consumption of Viral Content: A Quasi-Experimental Analysis Using Synthetic Controls

2020 | Information Systems Research | Citations: 0

Authors: Krijestorac, Haris; Garg, Rajiv; Mahajan, Vijay

Abstract: To inform product release and distribution strategies, research has analyzed cro ... Expand

Abstract: To inform product release and distribution strategies, research has analyzed cross-market spillovers in new product adoption. However, models that examine these effects for digital and viral media are still evolving. Given resistance to advertising, firms often seek to promote their own viral content to boost brand awareness. However, a key shortcoming of virality is its ephemeral nature. To gain insight into sustaining virality, we develop a quasi-experimental approach that estimates the backward spillover onto a focal platform by introducing a piece of content onto a new platform. We posit that introducing content to the audience of a new platform can generate word of mouth, which may affect its consumption within an earlier platform. We estimate these spillovers using data on 381 viral videos on 26 platforms (e.g., YouTube, Vimeo) and observe how consumption of videos on an initial “lead” platform is affected by their subsequent introduction onto “lag” platforms. This spillover is estimated as follows: for each multiplatform video, we compare its view growth after being introduced onto a new platform to that of a synthetic control based on similar single-platform videos. Analysis of 275 such spillover scenarios reveals that introducing a video onto a lag platform roughly doubles its subsequent view growth in the lead platform. This positive cross-platform spillover is persistent, bursty, and strongest in the first 42 days. We find that spillover is boosted when the video is consumed more in the lag platform, when the consumption rate peaks earlier in the lag platform, and when the lag platform targets a foreign market. Delaying a video’s introduction onto a lag platform affects spillover concavely, whereas its introduction onto additional platforms shows diminishing returns. We find further support for positive spillover through a small-scale randomized field experiment. Implications are discussed for platforms, content creators, and policy makers. Collapse

Topics: YouTube digital platform viral marketing digital media marketing management

Methods: synthetic control method experiment quasi experiment field experiment Kolmogorov-Smirnov test

Don't be so emotional: How social media communication affects potential applicants' engagement

2020 | European Conference On Information Systems | Citations: 0

Authors: Moser, Kilian; Tumasjan, Andranik; Cable, Dan

Abstract: ... Expand

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Topics: Twitter social media online labor market marketing information system viral marketing

Methods: regression analysis method support vector machine statistical hypothesis test machine learning qualitative content analysis

Theories: contract theory statistical learning theory

The Long-term Diffusion of Digital Platforms — An Agent-based Model

2019 | International Conference on Information Systems | Citations: 0

Authors: Schalowski, Jan; Barrot, Christian

Abstract: In recent years, many industries have experienced the rise of digital platforms ... Expand

Abstract: In recent years, many industries have experienced the rise of digital platforms (e.g., eBay, Uber, or Takeaway.com). A common characteristic of these concepts is that they focus on fragmented markets populated by many small firms, which often show a high fluctuation. However, established diffusion models based on Bass (1969) do not account for fluctuation in the market potential, although the exit of adopters and the entry of new firms could change the diffusion curve significantly. Thus, we propose an extension of the Bass Model to account for the exit and entry of (potential) adopters and empirically test this framework in a real-world setting. Using two decades of adopter data of leading digital platforms and information on the complete market potential, we employ agent-based models to analyze the effects of fluctuation on the platform diffusion. Initial results confirm the existence of high fluctuation and indicate relevant impacts on the diffusion curve. Collapse

Topics: digital platform social influence innovation management Python viral marketing

Methods: agent based simulation simulation

Detection of Spammers in Twitter marketing: A Hybrid Approach Using Social Media Analytics and Bio Inspired Computing

2018 | Information Systems Frontiers | Citations: 6

Authors: Aswani, Reema; Kar, Arpan Kumar; Vigneswara Ilavarasan, P.

Abstract: Customer engagement is drastically improved through Web 2.0 technologies, especi ... Expand

Abstract: Customer engagement is drastically improved through Web 2.0 technologies, especially social media platforms like Twitter. These platforms are often used by organizations for marketing, of which creation of numerous spam profiles for content promotion is common. The present paper proposes a hybrid approach for identifying the spam profiles by combining social media analytics and bio inspired computing. It adopts a modified K-Means integrated Levy flight Firefly Algorithm (LFA) with chaotic maps as an extension to Firefly Algorithm (FA) for spam detection in Twitter marketing. A total of 18,44,701 tweets have been analyzed from 14,235 Twitter profiles on 13 statistically significant factors derived from social media analytics. A Fuzzy C-Means Clustering approach is further used to identify the overlapping users in two clusters of spammers and non-spammers. Six variants of K-Means integrated FA including chaotic maps and levy flights are tested. The findings indicate that FA with chaos for tuning attractiveness coefficient using Gauss Map converges to a working solution the fastest. Further, LFA with chaos for tuning the absorption coefficient using sinusoidal map outperforms the rest of the approaches in terms of accuracy. Collapse

Topics: Twitter email spam social media social media analytics user-generated content

Methods: computational algorithm cluster analysis topic model literature study mixed method

Theories: chaos theory

Exploring the digitalization impact on consumer decision-making in retail banking

2018 | Electronic Markets | Citations: 0

Authors: Pousttchi, Key; Dehnert, Maik

Abstract: Retail banking has undergone a massive transformation in the last few years. A m ... Expand

Abstract: Retail banking has undergone a massive transformation in the last few years. A major aspect is changing consumer behavior. The aim of the paper is to better understand retail banking consumers regarding the impact of digitalization. Consequently, we acquired online consumer review data from Germany, the UK and US. We analyzed the data using coding techniques of grounded theory, supported by interdisciplinary literature to identify and categorize the relevant influence factors. The outcome of the paper is an integrated model of consumer decision-making in today’s retail banking along with four detailed partial models of the respective decision stages. Collapse

Topics: mobile application user friendliness decision making viral marketing website

Methods: qualitative coding grounded theory business process modeling theory development survey

Theories: technology acceptance model

Cross-Platform Spillover Effects in Consumption of Rich Digital Media

2017 | International Conference on Information Systems | Citations: 0

Authors: Krijestorac, Haris; Garg, Rajiv; Mahajan, Vijay; Ter Hofstede, Frenkel

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Topics: YouTube advertising management digital media sharing platform viral marketing

Methods: theoretical contribution computational algorithm longitudinal research descriptive statistic quasi experiment

Theories: diffusion of innovations theory

Design and Evaluation of Auto-ID Enabled Shopping Assistance Artifacts in Customers’ Mobile Phones: Two Retail Store Laboratory Experiments

2017 | Management Information Systems Quarterly | Citations: 0

Authors: Venkatesh, Viswanath; Aloysius, John; Hoehle, Hartmut; Burton, Scot

Abstract: There has been widespread use of auto-ID technologies. With the increasing diff ... Expand

Abstract: There has been widespread use of auto-ID technologies. With the increasing diffusion of smartphones, the potential to serve content to shoppers using auto-ID technologies is starting to receive interest. Using a design science approach, we design and build, theorize about, and compare six shopping assistance artifacts by manipulating the hardware design-barcode scanner versus radio frequency identification (RFID) reader-and content design-product information versus product review versus both. We theorize about how these artifact conditions will compare to a control condition (no shopping assistance artifact available) across three sets of outcomes: technology adoption, security beliefs, and shopping. We tested our propositions in two experiments-wherein the task was varied: general browsing and shopping (n = 227) versus goal-directed shopping (n = 221)-conducted in a retail store laboratory. The RFID reader was most favorably received in terms of technology adoption outcomes and shopping outcomes, although it was most negatively viewed in terms of security beliefs. We also found that the content design conditions (i.e., product information, product reviews, and both) were perceived favorably. In a post hoc analysis, we found a two-way interaction of hardware and content designs such that content fueled by RFID was perceived most favorably in terms of technology adoption and shopping outcomes, whereas it was most negatively viewed in terms of security beliefs. Interestingly, the two-way interaction was most pronounced in the goal-directed shopping condition such that the most positive effects were observed for RFID in combination with both product information and reviews. Collapse

Topics: radio frequency identification computer hardware technology adoption mobile application usage intention

Methods: experiment product review theory development design science design artifact

Theories: technology acceptance model