The extent of energy drink marketing on Canadian social media.

BACKGROUND: Caffeinated energy drink (CED) consumption among children and adolescents is a growing global public health concern due to its potential to produce adverse effects. CED marketing viewed by children and adolescents contributes to this problem as it increases consumption and favourable attitudes towards these high-caffeine and high-sugar products. This study aimed to describe the social media marketing of CEDs by estimating the frequency of user-generated and company-generated CED marketing and analyzing the marketing techniques used by Canadian CED brands on social media. METHODS: CED products and brands were identified using the list of CEDs that received a Temporary Marketing Authorization from Health Canada in June 2021. The data on the frequency, reach and engagement of CED-related posts created by users and Canadian CED brands on Facebook, Instagram, Twitter, Reddit, Tumblr, and YouTube were licensed from Brandwatch for 2020-2021. A content analysis was conducted to assess the marketing techniques used in Canadian CED company-generated posts using a coding manual. RESULTS: A total of 72 Canadian CED products were identified. Overall, there were 222,119 user-level mentions of CED products in total and the mentions reached an estimated total of 351,707,901 users across platforms. The most popular product accounted for 64.8% of the total user-level mentions. Canadian social media company-owned accounts were found for 27 CED brands. Two CED brands posted the most frequently on Twitter and accounted for the greatest reach, together making up 73.9% of the total company-level posts and reaching 62.5% of the total users in 2020. On Instagram/Facebook, the most popular brand accounted for 23.5% of the company-level posts and 81.3% of the reach between July and September 2021. The most popular marketing techniques used by Canadian CED brands were the use of viral marketing strategies (82.3% of Twitter posts and 92.5% of Instagram/Facebook posts) and the presence of teen themes (73.2% of Twitter posts and 39.4% of Instagram/Facebook posts). CONCLUSION: CED companies are extensively promoting their products across social media platforms using viral marketing strategies and themes that may appeal to adolescents. These findings may inform CED regulatory decision-making. Continued monitoring is warranted.

Local structure can identify and quantify influential global spreaders in large scale social networks.

Measuring and optimizing the influence of nodes in big-data online social networks are important for many practical applications, such as the viral marketing and the adoption of new products. As the viral spreading on a social network is a global process, it is commonly believed that measuring the influence of nodes inevitably requires the knowledge of the entire network. Using percolation theory, we show that the spreading process displays a nucleation behavior: Once a piece of information spreads from the seeds to more than a small characteristic number of nodes, it reaches a point of no return and will quickly reach the percolation cluster, regardless of the entire network structure; otherwise the spreading will be contained locally. Thus, we find that, without the knowledge of the entire network, any node's global influence can be accurately measured using this characteristic number, which is independent of the network size. This motivates an efficient algorithm with constant time complexity on the long-standing problem of best seed spreaders selection, with performance remarkably close to the true optimum.

Joint Selection of Influential Users and Locations under Target Region in Location-Based Social Networks.

Influence Maximization problem, selection of a set of users in a social network to maximize the influence spread, has received ample research attention in the social network analysis domain due to its practical applications. Although the problem has been extensively studied, existing works have neglected the location's popularity and importance along with influential users for product promotion at a particular region in Location-based Social Networks. Real-world marketing companies are more interested in finding suitable locations and influential users in a city to promote their product and attract as many users as possible. In this work, we study the joint selection of influential users and locations within a target region from two complementary perspectives; general and specific location type selection perspectives. The first is to find influential users and locations at a specified region irrespective of location type or category. The second perspective is to recommend locations matching location preference in addition to the target region for product promotion. To address general and specific location recommendations and influential users, we propose heuristic-based methods that effectively find influential users and locations for product promotion. Our experimental results show that it is not always an optimal choice to recommend locations with the highest popularity values, such as ratings, check-ins, and so, which may not be a true indicator of location popularity to be considered for marketing. Our results show that not only influential users are helpful for product promotion, but suitable influential locations can also assist in promoting products in the target region.

An Improved Influence Maximization Method for Online Advertising in Social Internet of Things.

Recently, a new subject known as the Social Internet of Things (SIoT) has been presented based on the integration the Internet of Things and social network concepts. SIoT is increasingly popular in modern human living, including applications such as smart transportation, online health care systems, and viral marketing. In advertising based on SIoT, identifying the most effective diffuser nodes to maximize reach is a critical challenge. This article proposes an efficient heuristic algorithm named Influence Maximization of advertisement for Social Internet of Things (IMSoT), inspired by real-world advertising. The IMSoT algorithm consists of two steps: selecting candidate objects and identifying the final seed set. In the first step, influential candidate objects are selected based on factors, such as degree, local importance value, and weak and sensitive neighbors set. In the second step, effective influence is calculated based on overlapping between candidate objects to identify the appropriate final seed set. The IMSoT algorithm ensures maximum influence and minimum overlap, reducing the spreading caused by the seed set. A unique feature of IMSoT is its focus on preventing duplicate advertising, which reduces extra costs, and considering weak objects to reach the maximum target audience. Experimental evaluations in both real-world and synthetic networks demonstrate that our algorithm outperforms other state-of-the-art algorithms in terms of paying attention to weak objects by 38%-193% and in terms of preventing duplicate advertising (reducing extra cost) by 26%-77%. Additionally, the running time of the IMSoT algorithm is shorter than other state-of-the-art algorithms.

Modeling and maximizing influence diffusion in social networks for viral marketing.

Modeling influence diffusion in social networks is an important challenge. We investigate influence-diffusion modeling and maximization in the setting of viral marketing, in which a node's influence is measured by the number of nodes it can activate to adopt a new technology or purchase a new product. One of the fundamental problems in viral marketing is to find a small set of initial adopters who can trigger the most further adoptions through word-of-mouth-based influence propagation in the network. We propose a novel multiple-path asynchronous threshold (MAT) model, in which we quantify influence and track its diffusion and aggregation. Our MAT model captures not only direct influence from neighboring influencers but also indirect influence passed along by messengers. Moreover, our MAT framework models influence attenuation along diffusion paths, temporal influence decay, and individual diffusion dynamics. Our work is an important step toward a more realistic diffusion model. Further, we develop an effective and efficient heuristic to tackle the influence-maximization problem. Our experiments on four real-life networks demonstrate its excellent performance in terms of both influence spread and time efficiency. Our work provides preliminary but significant insights and implications for diffusion research and marketing practice.

Internet blogs as a tourism marketing medium: A case study.

This study explores a 2003 Yahoo Anniversary Website-I left my heart in Aegean Sea-built by Justin, a Taiwanese engineer. Justin returns from the romantic Aegean Sea and puts 124 photographs he took onto a website to share with his friends. Unexpectedly, the website becomes a hit and the address achieves wide distribution via various chain e-mails. The site helps to promote tourism to Greece, a destination relatively unknown to most Taiwanese. This investigation explores why the site appeals to so many visitors, impresses them, and even draws them to plan travel to Greece. The article describes the effect of the website by analyzing messages left on the site. The AIDA model is useful for classifying holistic messages. The data demonstrate that the website generates desire and action in over 45% of its readers. These individuals either announce plans to visit Greece immediately or at some time in the foreseeable future. The website thus significantly influences browsers and indirectly promotes Greek tourism. Then, this article describes the key success factors of this popular website. Finally, the article presents suggestions and implications for the tourism industry and for nations seeking to promote tourism.

An Internet-Based Intervention to Promote Alcohol-Related Attitudinal and Behavioral Change Among Adolescents: Protocol of a Cluster Randomized Controlled Trial.

BACKGROUND: Underage drinking is a prevalent risk behavior and common public health problem. Research shows that alcohol abuse not only affects the quality of life of drinkers themselves. The problems resulting from underage drinking pose substantial costs to society as well. The proposed study will address underage drinking with the use of an Internet campaign, which is a cost-effective way of tackling the problem. OBJECTIVE: The aims of this study are to test the effectiveness of an online quiz competition in changing adolescents' alcohol-related attitudes and behavior and to explore the feasibility of using Internet viral marketing to reach a significant number of adolescents. METHODS: The study will constitute a cluster randomized controlled trial for 20 secondary schools (6720 Grade 7-9 students). Schools will be randomized to intervention or control arm with equal likelihood. Students in intervention schools will be invited to take part in the Internet campaign, whereas those in control schools will receive relevant promotional leaflets. RESULTS: Alcohol-related attitude and behavior will be the primary outcome measures. The results of the proposed study will provide evidence on the efficacy of an Internet intervention in modifying adolescents' attitudes and behavior and guide further investigation into the prevention of and intervention in such risk behaviors as underage drinking. The project was funded July 2015, enrollment started September 2015, and results are expected July 2017. CONCLUSIONS: With the Internet increasingly being recognized as a practical and cost-effective platform for health information delivery, the proposed Internet-based intervention is expected to be more effective in altering adolescents' alcohol-related attitudes and behaviors than traditional health promotion. CLINICALTRIAL: ClinicalTrials.gov NCT02450344; https://clinicaltrials.gov/ct2/show/NCT02450344 (Archived by WebCite at http://www.webcitation.org/6heB2zMBD).