Multimodal Popularity Prediction of Brand-related Social Media Posts

Mazloom, Masoud; Rietveld, Robert; Rudinac, Stevan; Worring, Marcel; Dolen, Willemijn van

doi:10.1145/2964284.2967210

Cited by 96 publications

(82 citation statements)

References 16 publications

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“…Social media messages are generally multimodal, such that they contain both text and images (Mazloom et al 2016). Both elements can signal message intentions.…”

Section: Image Acts and Consumer Sharingmentioning

confidence: 99%

“…For example, we find that the presence of readable text in social media images increases sharing, but we do not explore the types of message included in those images. Research on multimodal communication and picture mining (Liu et al 2017;Mazloom et al 2016) might offer some relevant insights for further research.…”

Section: Limitations and Further Research Directionsmentioning

confidence: 99%

See 1 more Smart Citation

Cutting through Content Clutter: How Speech and Image Acts Drive Consumer Sharing of Social Media Brand Messages

Ordenes

Grewal

Ludwig

et al. 2018

Journal of Consumer Research

222

109

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Consumer-to-consumer brand message sharing is pivotal for effective social media marketing. Even as companies join social media conversations and generate millions of brand messages, it remains unclear what, how, and when brand messages stand out and prompt sharing by consumers. With a conceptual extension of speech act theory, this study offers a granular assessment of brands' message intentions (i.e., assertive, expressive, or directive) and the effects on consumer sharing. A text mining study of more than two years of Facebook posts and Twitter tweets by well-known consumer brands empirically demonstrates the impacts of distinct message intentions on consumers' message sharing. Specifically, the use of rhetorical styles (alliteration and repetitions) and cross-message compositions enhance consumer message sharing. As a further extension, an image-based study demonstrates that the presence of visuals, or so-called image acts, increases the ability to account for message sharing. The findings explicate brand message sharing by consumers and thus offer guidance to content managers for developing more effective conversational strategies in social media marketing.

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“…Social media messages are generally multimodal, such that they contain both text and images (Mazloom et al 2016). Both elements can signal message intentions.…”

Section: Image Acts and Consumer Sharingmentioning

confidence: 99%

Section: Limitations and Further Research Directionsmentioning

confidence: 99%

Cutting through Content Clutter: How Speech and Image Acts Drive Consumer Sharing of Social Media Brand Messages

Ordenes

Grewal

Ludwig

et al. 2018

Journal of Consumer Research

222

109

View full text Add to dashboard Cite

show abstract

“…Scenarios for detecting Pornographic, Violence, and Medical images are directly based on the popular Google Vision API with pre-trained deep learning models. Google Vision API has been widely used by state-of-the-art approaches and has been experimentally demonstrated to be effective in flagging pornographic, violence, and medical images [16,18,27,58]. As experimentally demonstrated by Chen et al [16], Google Vision API can indeed outperform other image scanning services.…”

Section: Censoredmentioning

confidence: 96%

MadDroid: Characterizing and Detecting Devious Ad Contents for Android Apps

Liu

Wang

et al. 2020

Proceedings of the Web Conference 2020

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Advertisement drives the economy of the mobile app ecosystem. As a key component in the mobile ad business model, mobile ad content has been overlooked by the research community, which poses a number of threats, e.g., propagating malware and undesirable contents. To understand the practice of these devious ad behaviors, we perform a large-scale study on the app contents harvested through automated app testing. In this work, we first provide a comprehensive categorization of devious ad contents, including five kinds of behaviors belonging to two categories: ad loading content and ad clicking content. Then, we propose MadDroid, a framework for automated detection of devious ad contents. MadDroid leverages an automated app testing framework with a sophisticated ad view exploration strategy for effectively collecting ad-related network traffic and subsequently extracting ad contents. We then integrate dedicated approaches into the framework to identify devious ad contents. We have applied MadDroid to 40,000 Android apps and found that roughly 6% of apps deliver devious ad contents, e.g., distributing malicious apps that cannot be downloaded via traditional app markets. Experiment results indicate that devious ad contents are prevalent, suggesting that our community should invest more effort into the detection and mitigation of devious ads towards building a trustworthy mobile advertising ecosystem. CCS CONCEPTS• Security and privacy → Software and application security; • Information systems → Online advertising; • Human-centered computing → Ubiquitous and mobile computing.

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“…The technological and economic importance of popularity prediction motivate many researchers to notice this area [3] [4], the authors predict on-line message popularity by analyzing textual information. Besides image and text, some novel information such as time [5][24], sentiment [6] or even brand [7] [25] are also considered to guide the prediction.…”

Section: Popularity Predictionmentioning

confidence: 99%

How to Become Instagram Famous: Post Popularity Prediction with Dual-Attention

Zhang

Chen

Zhou

et al. 2018

2018 IEEE International Conference on Big Data (Big Data)

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With a growing number of social apps, people have become increasingly willing to share their everyday photos and events on social media platforms, such as Facebook, Instagram, and WeChat. In social media data mining, post popularity prediction has received much attention from both data scientists and psychologists. Existing research focuses more on exploring the post popularity on a population of users and including comprehensive factors such as temporal information, user connections, number of comments, and so on. However, these frameworks are not suitable for guiding a specific user to make a popular post because the attributes of this user are fixed. Therefore, previous frameworks can only answer the question "whether a post is popular" rather than "how to become famous by popular posts". In this paper, we aim at predicting the popularity of a post for a specific user and mining the patterns behind the popularity. To this end, we first collect data from Instagram. We then design a method to figure out the user environment, representing the content that a specific user is very likely to post. Based on the relevant data, we devise a novel dual-attention model to incorporate image, caption, and user environment. The dual-attention model basically consists of two parts, explicit attention for image-caption pairs and implicit attention for user environment. A hierarchical structure is devised to concatenate the explicit attention part and implicit attention part. We conduct a series of experiments to validate the effectiveness of our model and investigate the factors that can influence the popularity. The classification results show that our model outperforms the baselines, and a statistical analysis identifies what kind of pictures or captions can help the user achieve a relatively high "likes" number.

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Multimodal Popularity Prediction of Brand-related Social Media Posts

Cited by 96 publications

References 16 publications

Cutting through Content Clutter: How Speech and Image Acts Drive Consumer Sharing of Social Media Brand Messages

Cutting through Content Clutter: How Speech and Image Acts Drive Consumer Sharing of Social Media Brand Messages

MadDroid: Characterizing and Detecting Devious Ad Contents for Android Apps

How to Become Instagram Famous: Post Popularity Prediction with Dual-Attention

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