Multimodal Post Attentive Profiling for Influencer Marketing
Seungbae Kim,Jyun-Yu Jiang,Masaki Nakada,Jinyoung Han,Wei Wang +4 more
- pp 2878-2884
TLDR
A multimodal deep learning model that uses text and image information from social media posts to classify influencers into specific interests/topics and uses the attention mechanism to select the posts that are more relevant to the topics of influencers, thereby generating useful influencer representations.Abstract:
Influencer marketing has become a key marketing method for brands in recent years. Hence, brands have been increasingly utilizing influencers’ social networks to reach niche markets, and researchers have been studying various aspects of influencer marketing. However, brands have often suffered from searching and hiring the right influencers with specific interests/topics for their marketing due to a lack of available influencer data and/or limited capacity of marketing agencies. This paper proposes a multimodal deep learning model that uses text and image information from social media posts (i) to classify influencers into specific interests/topics (e.g., fashion, beauty) and (ii) to classify their posts into certain categories. We use the attention mechanism to select the posts that are more relevant to the topics of influencers, thereby generating useful influencer representations. We conduct experiments on the dataset crawled from Instagram, which is the most popular social media for influencer marketing. The experimental results show that our proposed model significantly outperforms existing user profiling methods by achieving 98% and 96% accuracy in classifying influencers and their posts, respectively. We release our influencer dataset of 33,935 influencers labeled with specific topics based on 10,180,500 posts to facilitate future research.read more
Citations
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On the dynamics of political discussions on Instagram: A network perspective
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TL;DR: It is shown that communities discussing political topics tend to be more engaged in the debate by writing longer comments, using more emojis, hashtags and negative words than in other subjects, and communities built around political discussions tend to been more dynamic, although top commenters remain active and preserve community membership over time.
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On the Dynamics of Political Discussions on Instagram: A Network Perspective
Carlos Henrique Gomes Ferreira,Carlos Henrique Gomes Ferreira,Carlos Henrique Gomes Ferreira,Fabricio Murai,Ana Paula Couto e Silva,Jussara M. Almeida,Martino Trevisan,Luca Vassio,Marco Mellia,Idilio Drago +9 more
TL;DR: In this article, the authors investigate the emergence of communities of co-commenters, i.e., groups of users who often interact by commenting on the same posts and may be driving the ongoing online discussions.
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Multimodal Marketing Intent Analysis for Effective Targeted Advertising
TL;DR: In this article , a novel multimodal-based marketing intent analysis scheme (MMIA) is proposed to estimate the marketing intent embedded in the multimodi-al contents of a piece of social news.
Book ChapterDOI
Detecting Engagement Bots on Social Influencer Marketing
Seungbae Kim,Jinyoung Han +1 more
TL;DR: In this article, a neural network-based model was proposed to detect the engagement bots from audiences of influencers, which outperformed well-known baseline methods by achieving 80% accuracy.
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