What Makes Online Content Viral?

Berger, Jonah; Milkman, Katherine L.

doi:10.2139/ssrn.1528077

Cited by 83 publications

(89 citation statements)

References 29 publications

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“…Our findings should be interpreted with the following limitations. The messages examined in this study are brief textual arguments extracted from televised antismoking public service announcements, and the content characteristics we tested are not exhaustive of all potential features of those public service announcements influencing the selection and retransmission behaviors, such as efficacy information (15,21,59) and psychophysiologically arousing audio/visual features (44,60,61). In addition, this study was an analog study, where selection was realistic (but forced) but retransmission was hypothetical using a self-reported measure of behavioral intention.…”

Section: Discussionmentioning

confidence: 99%

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Content Characteristics Driving the Diffusion of Antismoking Messages: Implications for Cancer Prevention in the Emerging Public Communication Environment

et al. 2013

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).This study examined how content characteristics of antitobacco messages affect smokers' selective exposure to and social sharing of those messages. Results from an experiment revealed that content features predicting smokers' selection of antismoking messages are different from those predicting whether those messages are shared. Antismoking messages smokers tend to select are characterized by strong arguments (odds ratio = 2.02, P = .02) and positive sentiments (odds ratio = 3.08, P = .03). Once selected, the messages more likely to be retransmitted by smokers were those with novel arguments (B = .83, P = .002) and positive sentiments (B = 1.65, P = .005). This research adds to the literature about the content characteristics driving the social diffusion of antitobacco messages and contributes to our understanding of the role of persuasive messages about smoking cessation in the emerging public communication environment. J Natl Cancer Inst Monogr 2013;47:182-187 Cigarette smoking remains the leading preventable cause of mortality in the United States and is a significant cancer risk factor. More than 90% of lung cancer is attributable to smoking tobacco, and tobacco use significantly increases the risk of developing all of the most commonly diagnosed forms of cancer (1). Thus, tobacco control efforts are critically important for cancer prevention. Tobacco control programs have historically relied heavily on traditional mass media to spread antitobacco messages (2-4). But Web 2.0 and the social media have resulted in rapid, profound changes in the media landscape (5-7). Implications for cancer prevention and tobacco control are just beginning to be understood.Rapid diffusion of new information technologies has created unprecedented opportunities for exposure to and sharing of antitobacco-related messages (ie, messages designed to prevent or curtail tobacco use). As of 2012, 81% of adults in the United States use the Internet, and 59% said they looked online for health-related topics (5). Of online adults, 67% reported using social networking sites such as Facebook or Twitter (8). According to a recent survey (9), 53% of US adults get news forwarded to them via e-mail or social networking sites; 36% share news with their social networks via those platforms. In sum, the production and dissemination of health information in general, and of antismoking information in particular, is no longer in the hands of a few experts or major corporations; individuals now actively seek out, generate, and share information across their social networks (7,10).For more than 2 decades, tobacco control researchers have examined how exposure to televised antitobacco advertising affects individuals' cigarette smoking attitudes and behaviors (2,(11)(12)(13)(14). Although television advertising remains a critical component of tobacco control campaigns, it is becoming increasingly important to measure and understand the impact of antitobacco-related information across digital media platforms and the interactions across these platforms...

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Section: Discussionmentioning

confidence: 99%

“…People tend to talk about unusual or surprising events to make sense of them (47,48). Previous research has found that surprising or counterintuitive news articles are more frequently shared (44,49) and that folktales and jokes are spread more widely if they have a contrasting event that creates surprise (50). …”

mentioning

confidence: 99%

Content Characteristics Driving the Diffusion of Antismoking Messages: Implications for Cancer Prevention in the Emerging Public Communication Environment

et al. 2013

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“…6 Later in the paper, we follow Berger and Milkman (2011) and analyze the nature of the content posted. 7 However, the split slightly overstates the mean-difference since it is driven by a few outliers.…”

Section: Datamentioning

confidence: 99%

Active Social Media Management: The Case of Health Care

Miller

Tucker

2013

Information Systems Research

154

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Given the demand for authentic personal interactions over social media, it is unclear how much firms should actively manage their social media presence. We study this question empirically in a healthcare setting. We show empirically that active social media management drives more user-generated content. However, we find that this is due to an increase in incremental user postings from an organization's employees rather than from its clients. This result holds when we explore exogenous variation in social media policies, employees and clients that are explained by medical marketing laws, medical malpractice laws and distortions in Medicare incentives. Further examination suggests that content being generated mainly by employees can be avoided if a firm's postings are entirely client-focused. However, empirically the majority of firm postings seem not to be specifically targeted to clients' interests, instead highlighting more general observations or achievements of the firm itself. We show that untargeted postings like this provoke activity by employees rather than clients. This may not be a bad thing, as employee-generated content may help with employee motivation, recruitment or retention, but it does suggest that social media should not be funded or managed exclusively as a marketing function of the firm.

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“…Dealing with diffusion through a social network, we assume that the knowledge spreads by (informational) contagion. This viral attitude is testified in many empirical works (Berger andMilkman 2012, Phelps et al 2004). From a modeling viewpoint, following recent literature on contagion applied to a social environment (see, for instance, Dai Pra and Tolotti 2009 and Barucci and Tolotti 2012), we assume that agent i becomes aware at a random time  i .…”

Section: The Modelmentioning

confidence: 93%

“…They point out that a hub's customer value has to include the effect on others. Focusing on individual-level psychological processes, Berger and Milkman (2012) show that emotions whose content evokes in individuals, help determine which cultural items succeed in the marketplace of ideas, that is at the macro-level. The relation between emotion and diffusion or virality is very complex and relevant in the marketing perspective.…”

Section: The Micro-foundation: Agent-based Modelsmentioning

confidence: 99%

How the Innovation Diffusion Models from the Past can Help us to Explain Marketing in the New Media Era

Colapinto

Sartori

Tolotti

2015

Looking Forward, Looking Back: Drawing on the Past to Shape the Future of Marketing

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Even if the rhetoric of the Internet and the new digital media seems to have radically changed our technological environment, historical recurrences are relevant tools in order to analyze the future marketing. We propose a new multi-stage model able to bridge two different approaches, namely the adoption models à la Bass and the recent line of research concerning agentbased innovation diffusion models. Our technology allows us to find a closed form equation for awareness and adoption, taking into account heterogeneous population.

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What Makes Online Content Viral?

Cited by 83 publications

References 29 publications

Content Characteristics Driving the Diffusion of Antismoking Messages: Implications for Cancer Prevention in the Emerging Public Communication Environment

Content Characteristics Driving the Diffusion of Antismoking Messages: Implications for Cancer Prevention in the Emerging Public Communication Environment

Active Social Media Management: The Case of Health Care

How the Innovation Diffusion Models from the Past can Help us to Explain Marketing in the New Media Era

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