Fashion and beauty brands leverage social media influencers to shape purchasing decisions, improve cost effectiveness, and reach wider audiences. New conventional wisdom has brands moving away from mega influencers toward micro influencers due to greater perceived relatability and trustworthiness. This study employs a novel computational approach integrating network analysis and computational text analysis to understand differences in content and its diffusion through mega and micro influencer Twitter networks. Findings debunk conventional wisdom that micro influencers can best fill unique roles by forging intimate, emotion-laden interpersonal connections. While micro influencers are more central to two-way dialogue within their networks, mega influencers garner more affect directed towards them indicating greater trust. Practical implications for the continued value of mega influencers and the identification and development of promising micro influencers are discussed.

Practitioners and scholars increasingly employ social media listening platforms (SMLPs) driven by artificial intelligence (AI) to extract actionable insights from large amounts of social media data informing research questions and brand strategy. Due to their proprietary nature, AI tools within SMLPs are “black boxes” that force users to accept results on blind faith, a source of concern in industry and academia. This study seeks to provide greater understanding of the strengths and weaknesses of SMLPs by assessing the AI-based results of leading SMLP Crimson Hexagon (now Brandwatch Consumer Research) against those of a standard human content analysis and an analysis conducted using Linguistic Inquiry and Word Count (LIWC). Analyzing a random 10,000-post sample of the conversation around the Nike “Dream Crazy” ad featuring Colin Kaepernick, findings reveal Crimson Hexagon’s AI tools to be woefully unreliable in terms of brand identification as well as detection of post and brand sentiment polarity, specific emotions, and brand outcomes, demonstrating the hazards of blindly relying upon conclusions drawn from black-box social media listening platforms. Findings highlight the need for researchers to examine algorithm documentation and training data sets, as well as assess AI-generated data prior to use in research models and decisions.

Present research builds upon native advertising research by examining the roles of advertising message relevance, consumer-brand relationship strength, and authorship. Two national experiments were conducted among active Twitter users ages 18–34. In Study 1 (N = 147), a 2 (disclosure: tweet/promoted tweet) X 2 (high-/low-ad message relevance) experiment showed that while disclosure had no effect on advertising outcomes, strong message relevance effects were present. Study 2 (N = 363), a 2 (weaker/stronger brand relationship strength) X 2 (disclosure: tweet/promoted tweet) X 3 (authorship: brand/influencer/peer) design, found that consumer–brand relationship strength also directly and positively influences attitude towards the brand, purchase intention and sharing intention; while significantly enhancing the positive message relevance impact on sharing intention, relevance’s impact on purchase intention was lessened. Greater consumer–brand relationship strength influence on attitude towards the brand and purchase intention was present when native ads were authored by peers rather than brands or influencers.

Brands often engage in cause involvement out of a sense of social responsibility. While these associations with causes can positively impact brand equity, brand loyalty, and brand-favorable consumer choice, it is less understood whether corporate cause involvement has the ability to create cause associations in consumers that transcend the brand’s involvement and lead to enduring cause-oriented behaviors. This study uses social media analytics and hierarchical linear regression to investigate the Twitter activity of a random sample of 3,090 individuals who engaged directly with the anti-bullying cause in direct response to Secret’s ‘Mean Stinks’ CSR advertising campaign. This research identifies the influence that those individuals’ campaign-specific consumer engagement behavior (i.e. volume of activity and length of participation time) in the brand’s cause campaign, as well as their engagement with the broader anti-bullying cause had on the persistence of their cause-related activity following the end of Secret’s active advertising of the cause. This is done in an effort to identify whether cause affiliations that are inspired and influenced by a brand can endure beyond the brand’s campaign, and ultimately comment on the longitudinal social influence of brand involvement in social causes.

Processes of viral diffusion are important in biological, technological, and social systems alike. Several mathematical models of infection have been developed to predict diffusion through networks, such as nonlinear dynamical systems (NLDSs). Such models generally offer accurate representations of real‐world diffusion, particularly for networks with static topologies. However, simulations of viral diffusion are computationally expensive, rendering them infeasible for large‐scale networks. Here, a new approach is shown that leverages quantum computing to make viral diffusion simulations feasible for large networks, independent of network topology. Simulations of an error‐free quantum circuit accurately modeled viral diffusion, with multivariate Euclidean distances from predicted infection probabilities capped near 8% for a network with N = 5 nodes and t = 20 time‐steps. This is sufficient accuracy to distinguish the relative susceptibility of nodes and to identify significant changes, such as periods of especially high susceptibility. The results illustrate the potential for quantum computational network simulation to provide accurate models of diffusion through large networks, an important real‐world application of quantum computing. The ability to simulate viral diffusion is invaluable for researchers across disciplines who aim to understand, anticipate, prepare for, and intervene in ongoing diffusion processes.

Online communities devoted to mobile games represent exemplary examples of competitive communities of practice, leveraging the competing forces of cooperation and competition to foster in-group solidarity and motivate members to further engage with the game and community. In this study, latent Dirichlet allocation was used to model conversation topics across over two million posts and comments made during a 3-year period on the r/grandorder subreddit, a competitive community of practice devoted to the mobile game Fate/Grand Order. In total, 20 distinct topics emerged spanning topical foci and emotional tones. The prevalence of certain topics, such as those related to gacha game mechanics, declined as community members mastered the practice and as the practice itself changed. Future studies should compare the structure and evolution of conversations across contexts, and they should likewise examine differences that emerge in discourse due to the relative balance of competitive and cooperative influences.