Modeling Multimedia Ad Exposure: The Role of Low-Level Audiovisual Features
This study investigates whether low-level audio and video features can explain the variance in multimedia exposure as measured by the Multimedia Advertising Exposure Scale (MMAES). The goal is to understand the role of these features in predicting exposure and explore whether incorporating nonlinear...
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| Format: | Article |
| Language: | English |
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IEEE
2025-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10858143/ |
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| author | Urban Burnik Andrej Kosir Gregor Strle |
| author_facet | Urban Burnik Andrej Kosir Gregor Strle |
| author_sort | Urban Burnik |
| collection | DOAJ |
| description | This study investigates whether low-level audio and video features can explain the variance in multimedia exposure as measured by the Multimedia Advertising Exposure Scale (MMAES). The goal is to understand the role of these features in predicting exposure and explore whether incorporating nonlinear relationships based on interactions can improve modeling. An observational study with young participants (N=287) evaluated exposure to eight video advertisements. Linear and polynomial regression models were used to predict MMAES scores using low-level features. Results indicated that polynomial models outperformed linear models, capturing complex interactions and providing better predictive accuracy. The best polynomial model explained 34.6% of the variance in MMAES scores, with an MAE of 0.207 and an RMSE of 0.269. Bootstrap analysis confirmed model robustness, showing lower error rates and stable coefficients for polynomial models. SHapley Additive exPlanations (SHAP) analysis highlighted key features and their interactions, improving interpretability. These findings underscore the importance of nonlinear relationships in modeling multimedia exposure, with implications for optimizing multimedia advertising strategies. |
| format | Article |
| id | doaj-art-6a04c592af484cebb3e308140f1bcef3 |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-6a04c592af484cebb3e308140f1bcef32025-02-07T00:01:42ZengIEEEIEEE Access2169-35362025-01-0113230912311010.1109/ACCESS.2025.353663310858143Modeling Multimedia Ad Exposure: The Role of Low-Level Audiovisual FeaturesUrban Burnik0https://orcid.org/0000-0002-8652-4977Andrej Kosir1https://orcid.org/0000-0001-6938-221XGregor Strle2https://orcid.org/0000-0003-1076-6707Faculty of Electrical Engineering, University of Ljubljana, Ljubljana, SloveniaFaculty of Electrical Engineering, University of Ljubljana, Ljubljana, SloveniaFaculty of Electrical Engineering, University of Ljubljana, Ljubljana, SloveniaThis study investigates whether low-level audio and video features can explain the variance in multimedia exposure as measured by the Multimedia Advertising Exposure Scale (MMAES). The goal is to understand the role of these features in predicting exposure and explore whether incorporating nonlinear relationships based on interactions can improve modeling. An observational study with young participants (N=287) evaluated exposure to eight video advertisements. Linear and polynomial regression models were used to predict MMAES scores using low-level features. Results indicated that polynomial models outperformed linear models, capturing complex interactions and providing better predictive accuracy. The best polynomial model explained 34.6% of the variance in MMAES scores, with an MAE of 0.207 and an RMSE of 0.269. Bootstrap analysis confirmed model robustness, showing lower error rates and stable coefficients for polynomial models. SHapley Additive exPlanations (SHAP) analysis highlighted key features and their interactions, improving interpretability. These findings underscore the importance of nonlinear relationships in modeling multimedia exposure, with implications for optimizing multimedia advertising strategies.https://ieeexplore.ieee.org/document/10858143/Multimedia advertising exposurelow-level audio featureslow-level video featurespredictive modelingmachine learning in advertisingconsumer behavior analysis |
| spellingShingle | Urban Burnik Andrej Kosir Gregor Strle Modeling Multimedia Ad Exposure: The Role of Low-Level Audiovisual Features IEEE Access Multimedia advertising exposure low-level audio features low-level video features predictive modeling machine learning in advertising consumer behavior analysis |
| title | Modeling Multimedia Ad Exposure: The Role of Low-Level Audiovisual Features |
| title_full | Modeling Multimedia Ad Exposure: The Role of Low-Level Audiovisual Features |
| title_fullStr | Modeling Multimedia Ad Exposure: The Role of Low-Level Audiovisual Features |
| title_full_unstemmed | Modeling Multimedia Ad Exposure: The Role of Low-Level Audiovisual Features |
| title_short | Modeling Multimedia Ad Exposure: The Role of Low-Level Audiovisual Features |
| title_sort | modeling multimedia ad exposure the role of low level audiovisual features |
| topic | Multimedia advertising exposure low-level audio features low-level video features predictive modeling machine learning in advertising consumer behavior analysis |
| url | https://ieeexplore.ieee.org/document/10858143/ |
| work_keys_str_mv | AT urbanburnik modelingmultimediaadexposuretheroleoflowlevelaudiovisualfeatures AT andrejkosir modelingmultimediaadexposuretheroleoflowlevelaudiovisualfeatures AT gregorstrle modelingmultimediaadexposuretheroleoflowlevelaudiovisualfeatures |