# Low-Level Video Features as Predictors of Consumer Engagement in Multimedia Advertisement
## 2 Related Work  
### Ad Exposure Effects and Consumer Engagement  
Consumer engagement is a multidimensional concept that encompasses the cognitive, psychological, and behavioral components of consumer-brand interaction [2; 3; 4; 7; 8]. It is inextricably linked to ad exposure and is the main factor in its effectiveness [2; 4]. Consumer engagement is key to understanding consumer behavior, along with memorability [9], attitude [10], and purchase intention [4]. Higher consumer engagement leads to more exposure, improves brand memorability and familiarity, and may positively affect consumer purchase intentions [2; 3; 4]. Therefore, consumer engagement is one of the most important metrics for quantifying ad exposure and its effectiveness, and more generally, brand attitudes and overall consumer behavior. Consequently, the cognitive, affective, and behavioral dimensions of engagement are increasingly considered in the digital advertising landscape [2; 4]. However, despite the growing trend toward measuring engagement, the field lacks unifying theories and models that provide empirical support for consumer ad engagement [4; 11]. An analysis of over 200 studies on media exposure conducted by [11] found that there is still no universally accepted conceptualization and operationalization of media exposure and its effects, including consumer engagement. As [2] point out, there is no one-size-fits-all solution because consumer engagement is based on context-specific experiences and settings. For example, some researchers operationalize engagement in terms of behavioral manifestations in the consumer-brand relationship [8]. Others consider engagement in terms of brand messages evoking passionate and affective responses in consumers [7] or enablingcreative experiences [9]. The cognitive and emotional dimensions of consumer engagement were conceptualized in [12; 13]. For example, [PERSON]. [13] conceptualized engagement in the context of emotional arousal and created a model based on patterns of physiological responses to multimedia stimuli. A more complex conceptualization of consumer engagement was provided by [12], who defined the consumer engagement framework as a multidimensional construct based on cognitive, emotional, experiential, and social dimensions. The behavioral and social dimensions of engagement were further conceptualized using the User Engagement Scale (UES) developed by [5]. The UES measures the subjective aspects of engagement across multiple dimensions: aesthetic appeal, focused attention, novelty, perceived usability, felt involvement, and endurability [5]. The above operationalizations of engagement depend on user feedback from user surveys, interviews, or self-reports. However, it is not always possible to collect user feedback, which is time-consuming, and self-reports have been shown to be unreliable, as they are prone to rater bias [14]. This is especially true for digital advertising services, such as ad-supported video streaming, where traditional methods for gathering user feedback are difficult, if not impossible, to implement [6; 10]. More direct methods for measuring consumer engagement are required to capture consumer engagement in real time, without interrupting the streaming experience. To this end, several novel approaches have been developed to directly predict engagement based on machine learning and feature modeling of the content itself. For example, [PERSON]. [15] proposed a graph-embedding model called DeepWalk to predict the video engagement of an ad to detect ad fraud. This model can detect fake videos and fraud patterns in videos for well-known brands. More generally, [PERSON]. [16] proposed an automatic approach for processing and evaluating learner engagement. They developed a prediction model for context-agnostic engagement based on the video features of learning content and engagement signals.