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

Abstract

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.

Salienteye: Maximizing Engagement While Maintaining Artistic Style on Instagram Using Deep Neural Networks

“…Previous work on analyzing engagement on Instagram [5,13,17,18,24,26,27] treated images posted on different time periods the same way. However, as an account gets older and gathers more followers, the average number of likes for new photos posted goes up.…”

“…Zhu et al [2], show that photos using filters are more likely to be viewed and commented on, while Bakhshi et al [1], show that photos with faces attract more likes and comments. Several works have attempted to predict engagement using various combinations of text-based, network-based, and user-based features [5,6,10,13,17,18,24,26,27]. However, all of these works use a large dataset, containing many users.…”

“…Mazloom et al [15,16] have more granular models of engagement, looking at category-specific posts. The work that gets closest to having truly user-specific models of engagement is the work of Zhang et al, [27], however, they also train their models on a dataset from a few hundred users.…”

Narcissism and fame: a complex network model for the adaptive interaction of digital narcissism and online popularity

“…In this section, we analyze the behavior of Instagram users with respect to an increase/ decrease in the average number of likes. As per hypothesis, a user seeks the opportunity of self-promotion to get compliments or likes (Holtzman et al 2010;Paramboukis et al 2016;Zhang et al 2018). As addressed by an Instagram user:…”

Evaluation of COVID-19 spread effect on the Commercial Instagram Posts (CIPs) using ANN: a case study on Holy Shrine, Mashhad, Iran

“…Therefore, a combination of Instagram posts data -i.e., captions, hashtags, comments, photos, and videos -was commonly employed as the input features of neural networks in past research. In this regard, having considered users' environment on Instagram -i.e., the content users are most likely to post - (Zhang et al 2019) presented a dual-attention model based on neural networks to integrate image-caption pairs with the user environment on Instagram for post popularity prediction of a particular user. Illustration of non-suicidal self-injury (NSSI) on Instagram has been explored by (Scherr et al 2019) by developing an image-recognition Convolutional Neural Network (CNN) detecting the presence or absence of NSSI in digital pictures.…”