Identifying sequential influence in predicting engagement of online social marketing for video games
Advancement of online social networks has seen digital marketing use platforms like YouTube and Twitch as key levers for video games marketing. Identifying key influencer factors in these emerging platforms can both deliver better understanding of user behavior in consumption and engagement towards...
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| Format: | Conference or Workshop Item |
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2021
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| _version_ | 1796866211593584640 |
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| author | Chia, Joseph Wei Chen Mohd. Azmi Ais, Nurulhuda Firdaus |
| author_facet | Chia, Joseph Wei Chen Mohd. Azmi Ais, Nurulhuda Firdaus |
| author_sort | Chia, Joseph Wei Chen |
| collection | ePrints |
| description | Advancement of online social networks has seen digital marketing use platforms like YouTube and Twitch as key levers for video games marketing. Identifying key influencer factors in these emerging platforms can both deliver better understanding of user behavior in consumption and engagement towards marketing on social platforms and deliver great business value towards video game makers. However, data sparsity and topic maturity has made it difficult to identify user behavior over a sequence of different marketing videos, with a key challenge being identifying key features and distinguishing their contribution to the measure that defines sustained engagement over sequential marketing. This paper presents a method to understand sequential behavioral patterns by extracting features from marketing frameworks and develop a supervised model that takes all the features into consideration to identify the best contributing features to predicting engagement that delivers sustained interest for the next video in a series of marketing videos on YouTube. Experiment results on dataset demonstrate the proposed model is effective within constraint. |
| first_indexed | 2024-03-05T21:08:45Z |
| format | Conference or Workshop Item |
| id | utm.eprints-96378 |
| institution | Universiti Teknologi Malaysia - ePrints |
| last_indexed | 2024-03-05T21:08:45Z |
| publishDate | 2021 |
| record_format | dspace |
| spelling | utm.eprints-963782022-07-18T10:31:19Z http://eprints.utm.my/96378/ Identifying sequential influence in predicting engagement of online social marketing for video games Chia, Joseph Wei Chen Mohd. Azmi Ais, Nurulhuda Firdaus QA75 Electronic computers. Computer science Advancement of online social networks has seen digital marketing use platforms like YouTube and Twitch as key levers for video games marketing. Identifying key influencer factors in these emerging platforms can both deliver better understanding of user behavior in consumption and engagement towards marketing on social platforms and deliver great business value towards video game makers. However, data sparsity and topic maturity has made it difficult to identify user behavior over a sequence of different marketing videos, with a key challenge being identifying key features and distinguishing their contribution to the measure that defines sustained engagement over sequential marketing. This paper presents a method to understand sequential behavioral patterns by extracting features from marketing frameworks and develop a supervised model that takes all the features into consideration to identify the best contributing features to predicting engagement that delivers sustained interest for the next video in a series of marketing videos on YouTube. Experiment results on dataset demonstrate the proposed model is effective within constraint. 2021 Conference or Workshop Item PeerReviewed Chia, Joseph Wei Chen and Mohd. Azmi Ais, Nurulhuda Firdaus (2021) Identifying sequential influence in predicting engagement of online social marketing for video games. In: 6th International Conference on Soft Computing in Data Science, SCDS 2021, 2 November 2021 - 3 November 2021, Virtual, Online. http://dx.doi.org/10.1007/978-981-16-7334-4_31 |
| spellingShingle | QA75 Electronic computers. Computer science Chia, Joseph Wei Chen Mohd. Azmi Ais, Nurulhuda Firdaus Identifying sequential influence in predicting engagement of online social marketing for video games |
| title | Identifying sequential influence in predicting engagement of online social marketing for video games |
| title_full | Identifying sequential influence in predicting engagement of online social marketing for video games |
| title_fullStr | Identifying sequential influence in predicting engagement of online social marketing for video games |
| title_full_unstemmed | Identifying sequential influence in predicting engagement of online social marketing for video games |
| title_short | Identifying sequential influence in predicting engagement of online social marketing for video games |
| title_sort | identifying sequential influence in predicting engagement of online social marketing for video games |
| topic | QA75 Electronic computers. Computer science |
| work_keys_str_mv | AT chiajosephweichen identifyingsequentialinfluenceinpredictingengagementofonlinesocialmarketingforvideogames AT mohdazmiaisnurulhudafirdaus identifyingsequentialinfluenceinpredictingengagementofonlinesocialmarketingforvideogames |