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Popular Tag Recommendation by Neural Network in Social Media.

Mohammad Jafari Sadr1, Seyedeh Leili Mirtaheri2, Sergio Greco3

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This study developed a novel method for generating popular Instagram hashtags by combining image classification and a GPT-2 language model. The new algorithm significantly improves hashtag relevance and trendiness for viral marketing.

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Area of Science:

  • Computer Science
  • Social Science
  • Marketing

Background:

  • Social media success relies on more than just visuals.
  • Characterizing photos for viral marketing and public appeal is crucial.
  • Instagram data provides insights into popular content.

Purpose of the Study:

  • To determine optimal methods for characterizing photos for viral marketing.
  • To develop an algorithm for generating popular and appealing Instagram hashtags.
  • To explore the dual applications of content popularity in social science and marketing.

Main Methods:

  • Crawled 570,000 Instagram photos and 1.4 million hashtags.
  • Trained a ResNet model for multilabel image classification.
  • Developed a GPT-2 language model for generating popular hashtags based on image features.

Main Results:

  • The combined approach of image classification and GPT-2 significantly enhances hashtag generation.
  • The developed algorithm produces 11% more relevant, acceptable, and trending hashtags compared to baseline models.
  • Identified key components and features of popular social media photos.

Conclusions:

  • This research offers a novel method for creating viral marketing content through AI-driven hashtag generation.
  • The findings have implications for both social science research on consumer preferences and marketing strategies.
  • The study demonstrates the practical application of advanced AI models for social media optimization.