SoMin.ai: Social Multimedia Influencer Discovery Marketplace Aleksandr Farseev ITMO Univerity St. Petersburg, Russia farseev@gmail.com Kirill Lepikhin Social Miners Research Singapore kirill@somin.ai Hendrik Schwartz Social Miners Research Singapore hendrik@somin.ai Eu Khoon Ang Social Miners Singapore eukhoon@somin.ai Kenny Powar Rebel Owl Singapore kpowar@rebel-owl.com ABSTRACT In this technical demonstration, we showcase the frst ai-driven so- cial multimedia infuencer discovery marketplace, called SoMin [4]. The platform combines advanced data analytics and behavioral sci- ence to help marketers fnd, understand their audience and engage the most relevant social media micro-infuencers at a large scale. SoMin harvests brand-specifc life social multimedia streams in a specifed market domain, followed by rich analytics and semantic- based infuencer search. The Individual User Profling models ex- trapolate the key personal characteristics of the brand audience, while the infuencer retrieval engine reveals the semantically-matching social media infuencers to the platform users. The infuencers are matched in terms of both their-posted content and social media audiences, while the evaluation results demonstrate an excellent performance of the proposed recommender framework. By lever- aging infuencers at a large scale, marketers will be able to execute more efective marketing campaigns of higher trust and at a lower cost. ACM Reference Format: Aleksandr Farseev, Kirill Lepikhin, Hendrik Schwartz, Eu Khoon Ang, and Kenny Powar. 2018. SoMin.ai: Social Multimedia Infuencer Discov- ery Marketplace. In 2018 ACM Multimedia Conference (MM ’18), October 22–26, 2018, Seoul, Republic of Korea. ACM, New York, NY, USA, 3 pages. https://doi.org/10.1145/3240508.3241387 1 INTRODUCTION The past decade has testifed a rapid growth of the Internet. One can observe the drastic expansion of social networking services, where millions of users publish and consume information regu- larly. Built upon such growth, social media marketing industry has correspondingly developed its capabilities of helping marketers in content personalization and deliverance [11ś13]. However, the growing amount of irrelevant content, such as unrelated advertise- ment and spam, made social media users more and more reluctant towards perceiving sponsored search results and online advertise- ment, such as łGoogle AdWordsž [10] and łFacebook Sponsored Adsž [1]. Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for proft or commercial advantage and that copies bear this notice and the full citation on the frst page. Copyrights for third-party components of this work must be honored. For all other uses, contact the owner/author(s). MM ’18, October 22–26, 2018, Seoul, Republic of Korea © 2018 Copyright held by the owner/author(s). ACM ISBN 978-1-4503-5665-7/18/10. https://doi.org/10.1145/3240508.3241387 To mitigate such customer skepticism, marketers often leverage on human-centric content delivery channels, where Infuencer Mar- keting clearly dominates over other marketing strategies. Indeed, it has been shown that 92% of consumers are more likely to trust brands that advertise via infuencer channels [17] rather than those who has adopted conventional marketing strategies. Unfortunately, the limited availability of infuencer search platforms and the ab- sence of audience-based and content-based infuencer matching technology, result in tremendous amounts of manual work per- formed, the corresponding high marketing agency service costs and low efciency of the conducted marketing campaigns. Aiming at bridging the aforementioned research and industrial gaps, in this technical demonstration we propose an online infu- encer discovery platform that would perform infuencer matching at both content and audience levels simultaneously. To accomplish such semantic search, we have utilized our previously-proposed multi-source re-ranking approach [6], which is able to perform well-balanced and source-consistent recommendation over multi- ple data representations. The content representation was gained via extracting multi-modal hot topics, named entities, and image con- cepts [15] from recent infuencer-posted and brand-intended con- tent, while the audience representation was obtained via computing distributions of the brand and infuencer social media followers over automatically-profled behavioral user attributes [3, 7, 8]. The above technology was integrated into the cloud-based social mul- timedia infuencer discovery marketplace SoMin, which delivers semantic-based infuencer search to the corporate users. The overall data processing pipeline in SoMin is as follows: (a) the distributed cloud crawlers harvest recently posted user-generated content (UGC) from multiple social networks with respect to a particular brand and its corresponding social media infuencers [2]; (b) user profles are predicted via SoMin’s Social Multimedia Ana- lytics API endpoints [15] for all brand’s and infuencers’ followers, which allows for drawing brand-specifc and infuencer-specifc bahavioural audience distributions; (c) multi-source multi-modal topic, named entity recognition, as well as image concept detec- tion models [15] extract textual and visual content representations, which are used for drawing infuencers’ semantic content distri- butions as well as the semantic description of the brand-intended marketing message; and (d) the multi-modal re-ranking engine [6] recommends semantically-matching social media infuencers with respect to the brand-intended marketing message and its audience. To the best of our knowledge, SoMin is the frst ai-driven social