Consumer emotions and brand awareness are closely linked to the online advertising experience. Interactivity can enhance consumers’ engagement with social media sites and brands. However, there is a lack of research on evaluating consumers’ experience in social media interactive campaigns. To address this gap, the study proposes using mouse tracking as an unobtrusive method to collect consumers’ behavioural responses while interacting with gamified campaigns. The study involved 21 users who completed two experimental tasks, during which mouse data and explicit self-reported responses about their emotional states, brand awareness, and purchase intent were collected. Thirteen mouse features were calculated, including time between clicks, time between movements, speed, acceleration, total number of clicks, total number of movements, and total number of pauses. The study found that fast movements and a low number of actions were associated with a positive user experience in terms of brand awareness and intention to buy or recommend the product, while slow movements and long or frequent pauses were associated with negative emotions such as stress, frustration, and confusion. The findings indicate that mouse tracking can be a valuable tool for assessing brand engagement and the emotional dimensions of users’ interactions with gamified marketing campaigns.

This paper aims to provide the reader with a comprehensive background for understanding current knowledge on the use of non-intrusive Mobile Sensing methodologies for emotion recognition in Smartphone devices. We examined the literature on experimental case studies conducted in the domain during the past six years (2015–2020). Search terms identified 95 candidate articles, but inclusion criteria limited the key studies to 30. We analyzed the research objectives (in terms of targeted emotions), the methodology (in terms of input modalities and prediction models) and the findings (in terms of model performance) of these published papers and categorized them accordingly. We used qualitative methods to evaluate and interpret the findings of the collected studies. The results reveal the main research trends and gaps in the field. The study also discusses the research challenges and considers some practical implications for the design of emotion-aware systems within the context of Distance Education.

Emotion recognition is essential for assessing human emotional states and predicting user behavior to provide appropriate and personalized feedback. The wide range of Smartphones with accelerometers, microphones, GPSs, gyroscopes, and more motivate researchers to explore the automatic emotion detection through Smartphone sensors. To this end, mobile sensing can facilitate the data retrieval process in a non-intrusive way without disturbing the user's experience. This study seeks to contribute to the field of non-intrusive mobile sensing for emotion recognition by detecting user emotions via accelerometer and gyroscope sensors in Smartphones. A prototype gaming app was designed and a sensor log app for Android OS was used to monitor the users’ sensor data while interacting with the game. The recorded data from 40 users was processed and used to train different classifiers for two emotions: a positive (enjoyment) and a negative (frustration) one. The validation study demonstrates a high prediction of 87.90% for enjoyment and 89.45% for frustration. Our findings indicate that by analyzing accelerometer and gyroscope data, it is possible to make efficient predictions of a user's emotional state. The proposed model and its empirical development and validation are described in this paper.