Sharing the News: Effects of Informational Utility and Opinion Leadership on Online News Sharing

This study examined the joint effect of message and personality attributes on online news sharing. In two experiments (N = 270; N = 275) readers indicated their likelihood to share news representing two content domains and three informational utility dimensions. A moderated mediation path analysis was used. On average, news consumers shared news containing informational utility. Opinion leaders shared news irrespective of informational utility because they discerned informational utility in news that, objectively speaking, lacked such utility. In one experiment, opinion leaders also were more likely than non-leaders to share news perceived to contain informational utility

AI-enabled exploration of Instagram profiles predicts soft skills and personality traits to empower hiring decisions

It does not matter whether it is a job interview with Tech Giants, Wall Street firms, or a small startup; all candidates want to demonstrate their best selves or even present themselves better than they really are. Meanwhile, recruiters want to know the candidates' authentic selves and detect soft skills that prove an expert candidate would be a great fit in any company. Recruiters worldwide usually struggle to find employees with the highest level of these skills. Digital footprints can assist recruiters in this process by providing candidates' unique set of online activities, while social media delivers one of the largest digital footprints to track people. In this study, for the first time, we show that a wide range of behavioral competencies consisting of 16 in-demand soft skills can be automatically predicted from Instagram profiles based on the following lists and other quantitative features using machine learning algorithms. We also provide predictions on Big Five personality traits. Models were built based on a sample of 400 Iranian volunteer users who answered an online questionnaire and provided their Instagram usernames which allowed us to crawl the public profiles. We applied several machine learning algorithms to the uniformed data. Deep learning models mostly outperformed by demonstrating 70% and 69% average Accuracy in two-level and three-level classifications respectively. Creating a large pool of people with the highest level of soft skills, and making more accurate evaluations of job candidates is possible with the application of AI on social media user-generated data

Consumer judgment and forecasting using online word-of-mouth

Empowered by information technology, modern consumers increasingly rely upon online word-of-mouth (WOM--e.g., product reviews) to guide their purchase decisions. This dissertation investigates how WOM information is processed by consumers and its downstream consequences. First, the value of specific types of word-of-mouth information (e.g., numeric ratings, text commentary, or both) was explored for making forecast. After proposing an anchoring-and-adjustment framework for the utilization of WOM to inform consumer forecasts, I support this framework with a series of experiments. Results demonstrate that the relative forecasting advantage of different information types is a function of the extent to which consumer and reviewer have similar product-level preferences ('source-receiver similarity'). Second, I investigate the process by which dispersion--the degree to which opinions are divided for a product or service--in WOM is interpreted. Using an attribution-based approach, I argue that the effect of WOM dispersion is dependent on the perceived cause of that dispersion, which is systematically related to perceptions of preference heterogeneity in a product category. For products for which preferences are expected to vary, dispersion is likely to be attributed to the reviewers rather than the product itself, and therefore tolerated. I provide evidence for my hypotheses in a series of experiments where WOM dispersion is manipulated and respondents make choices and indicate purchase intentions.PhDCommittee Chair: Bond, Samuel D.; Committee Member: Feldman, Jack M.; Committee Member: Hamilton, Ryan; Committee Member: Lurie, Nicholas H.; Committee Member: Van Ittersum, Koer

The Social Diffusion of Influence Among Adolescents: Group Interaction in a Chat Room Environment About Antidrug Advertisements

One route to influence in mass communication campaigns to reduce risky behavior is through interpersonal discussion of the content of the campaign and other behaviors pertinent to those targeted by the campaign. The goal of this study was to test the effects of online group interaction among adolescents about anti-marijuana advertisements on relevant attitudes and behaviors. A between subjects post only experimental design was used to test two crossed factors, online chat and strength of arguments in antidrug ads. A sample of 535 students was randomly assigned to one of four conditions: chat and strong argument ads, chat and weak argument ads, no chat and strong argument ads, and no chat and weak argument ads. The group interactions about antidrug ads lead to negative effects such that those who chatted reported more pro-marijuana attitudes and subjective normative beliefs than those who just viewed the ads. No support was found for the hypothesis that strong argument ads would result in more antidrug beliefs relative to weak argument ads in either the chat or the no chat conditions. Overall, these findings suggest that viewing antidrug ads and discussing them with peers may result in deleterious effects in adolescents

The end of stigma? Understanding the dynamics of legitimisation in the context of TV series consumption

This research contributes to prior work on stigmatisation by looking at stigmatisation and legitimisation as social processes in the context of TV series consumption. Using in-depth interviews, we show that the dynamics of legitimisation are complex and accompanied by the reproduction of existing stigmas and creation of new stigmas

All click, no action? Online action, efficacy perceptions, and prior experience combine to affect future collective action

This is the author accepted manuscript. The final version is available from Elsevier via the DOI in this recordSocial media is increasingly used for social protest, but does internet-enabled action lead to ‘slacktivism’ or promote increased activism? We show that the answer to this question depends on prior level of activism, and on beliefs about the effectiveness of individual contribution to the collective campaign. Internet-enabled action was varied quasi-experimentally, with participants (n = 143) choosing whether or not to share a campaign on social media. Participants were then informed that sharing on social media had a big (high action efficacy) or small (low action efficacy) impact on achieving the campaign's goal. Prior levels of activism were measured before the experiment, and general levels of collective action were measured one week after the experiment. Taking internet-enabled action for one campaign increased future activism for other campaigns – but only in individuals who were already active and who perceived their actions to be an effective contribution to the campaign.This work was supported by the Defence Science and Technology Laboratory [grant number DSTLX-100074625]

Culture and Social Media

博士(文学)神戸市外国語大

Attractability and Virality: The Role of Message Features and Social Influence in Health News Diffusion

What makes health news articles attractable and viral? Why do some articles diffuse widely by prompting audience selections (attractability) and subsequent social retransmissions (virality), while others do not? Identifying what drives social epidemics of health news coverage is crucial to our understanding of its impact on the public, especially in the emerging media environment where news consumption has become increasingly selective and social. This dissertation examines how message features and social influence affect the volume and persistence of attractability and virality within the context of the online diffusion of New York Times (NYT) health news articles. The dissertation analyzes (1) behavioral data of audience selections and retransmissions of the NYT articles and (2) associated article content and context data that are collected using computational social science approaches (automated data mining; computer-assisted content analysis) along with more traditional methods (manual content analysis; message evaluation survey). Analyses of message effects on the total volume of attractability and virality show that articles with high informational utility and positive sentiment invite more frequent selections and retransmissions, and that articles are also more attractable when presenting controversial, emotionally evocative, and familiar content. Furthermore, these analyses reveal that informational utility and novelty have stronger positive associations with email-specific virality, while emotion-related message features, content familiarity, and exemplification play a larger role in triggering social media-based retransmissions. Temporal dynamics analyses demonstrate social influence-driven cumulative advantage effects, such that articles which stay on popular-news lists longer invite more frequent subsequent selections and retransmissions. These analyses further show that the social influence effects are stronger for articles containing message features found to enhance the total volume of attractability and virality. This suggests that those synergistic interactions might underlie the observed message effects on total selections and retransmissions. Exploratory analyses reveal that the effects of social influence and message features tend to be similar for both (1) the volume of audience news selections and retransmissions and (2) the persistence of those behaviors. However, some message features, such as expressed emotionality, are relatively unique predictors of persistence outcomes. Results are discussed in light of their implications for communication research and practice