Conceptualizing an omnichannel approach for social marketing under the assumptions of the transtheoretical model of change

Background: Digital technologies are important touchpoints to stimulate marketing audiences. In the field of social marketing, digital marketing is considered important, but has been mainly used to raise awareness of social causes. Focus of the Article: This paper considers the conceptualization of a model to conduct the conversion of behavior change, using both offline and digital marketing techniques. Research Question: The paper seeks to investigate existing research on how digital marketing concepts can be integrated into a social marketing strategy. Approach: The proposed conceptual model follows the process of the stages of change and considers the theoretical social marketing frameworks, applying the assumptions of citizens’ journey and the use of adequate digital and physical touchpoints to convert behavior. Importance to the Social Marketing Field: The model offers theoretical advances for social marketing, going beyond the stage of raising awareness of social causes in social networks, and integrates the assumptions of an omnichannel strategy for social marketing interventions focused on behavior change conversion. Methods: The paper follows the benchmark method of theories to build a conceptual model. Results: The Omnichannel Social Marketing Model Through Stages of Change presents adequate digital and physical marketing techniques for the different stages of the social change process. Recommendations for Research or Practice: The model can be used in future research to measure the effectiveness of social marketing, considering the inclusion of digital technologies and marketing techniques in social marketing strategy. The model also guides social marketing managers in using both digital and offline marketing techniques in an integrated and strategic manner for effective and long-term conversion of change. Future research can apply the model to social marketing cases to generalize its application. Limitations: The conceptual model is designed in a bottom-up approach, based on the literature review.FCT - Fundação para a Ciência e a Tecnologia(UIDB/04647/2020

Social Visual Behavior Analytics for Autism Therapy of Children Based on Automated Mutual Gaze Detection

Social visual behavior, as a type of non-verbal communication, plays a central role in studying social cognitive processes in interactive and complex settings of autism therapy interventions. However, for social visual behavior analytics in children with autism, it is challenging to collect gaze data manually and evaluate them because it costs a lot of time and effort for human coders. In this paper, we introduce a social visual behavior analytics approach by quantifying the mutual gaze performance of children receiving play-based autism interventions using an automated mutual gaze detection framework. Our analysis is based on a video dataset that captures and records social interactions between children with autism and their therapy trainers (N=28 observations, 84 video clips, 21 Hrs duration). The effectiveness of our framework was evaluated by comparing the mutual gaze ratio derived from the mutual gaze detection framework with the human-coded ratio values. We analyzed the mutual gaze frequency and duration across different therapy settings, activities, and sessions. We created mutual gaze-related measures for social visual behavior score prediction using multiple machine learning-based regression models. The results show that our method provides mutual gaze measures that reliably represent (or even replace) the human coders' hand-coded social gaze measures and effectively evaluates and predicts ASD children's social visual performance during the intervention. Our findings have implications for social interaction analysis in small-group behavior assessments in numerous co-located settings in (special) education and in the workplace.Comment: Accepted to IEEE/ACM international conference on Connected Health: Applications, Systems and Engineering Technologies (CHASE) 202

Detect Depression from Social Networks with Sentiment Knowledge Sharing

Social network plays an important role in propagating people's viewpoints, emotions, thoughts, and fears. Notably, following lockdown periods during the COVID-19 pandemic, the issue of depression has garnered increasing attention, with a significant portion of individuals resorting to social networks as an outlet for expressing emotions. Using deep learning techniques to discern potential signs of depression from social network messages facilitates the early identification of mental health conditions. Current efforts in detecting depression through social networks typically rely solely on analyzing the textual content, overlooking other potential information. In this work, we conduct a thorough investigation that unveils a strong correlation between depression and negative emotional states. The integration of such associations as external knowledge can provide valuable insights for detecting depression. Accordingly, we propose a multi-task training framework, DeSK, which utilizes shared sentiment knowledge to enhance the efficacy of depression detection. Experiments conducted on both Chinese and English datasets demonstrate the cross-lingual effectiveness of DeSK

Amplifying the Music Listening Experience through Song Comments on Music Streaming Platforms

Music streaming services are increasingly popular among younger generations who seek social experiences through personal expression and sharing of subjective feelings in comments. However, such emotional aspects are often ignored by current platforms, which affects the listeners' ability to find music that triggers specific personal feelings. To address this gap, this study proposes a novel approach that leverages deep learning methods to capture contextual keywords, sentiments, and induced mechanisms from song comments. The study augments a current music app with two features, including the presentation of tags that best represent song comments and a novel map metaphor that reorganizes song comments based on chronological order, content, and sentiment. The effectiveness of the proposed approach is validated through a usage scenario and a user study that demonstrate its capability to improve the user experience of exploring songs and browsing comments of interest. This study contributes to the advancement of music streaming services by providing a more personalized and emotionally rich music experience for younger generations.Comment: In the Proceedings of ChinaVis 202

Responsible Human-Robot Interaction with Anthropomorphic Service Robots: State of the Art of an Interdisciplinary Research Challenge

Anthropomorphic service robots are on the rise. The more capable they become and the more regular they are applied in real-world settings, the more critical becomes the responsible design of human-robot interaction (HRI) with special attention to human dignity, transparency, privacy, and robot compliance. In this paper we review the interdisciplinary state of the art relevant for the responsible design of HRI. Furthermore, directions for future research on the responsible design of HRI with anthropomorphic service robots are suggested

A Novel Multimodal Approach for Studying the Dynamics of Curiosity in Small Group Learning

Curiosity is a vital metacognitive skill in educational contexts, leading to creativity, and a love of learning. And while many school systems increasingly undercut curiosity by teaching to the test, teachers are increasingly interested in how to evoke curiosity in their students to prepare them for a world in which lifelong learning and reskilling will be more and more important. One aspect of curiosity that has received little attention, however, is the role of peers in eliciting curiosity. We present what we believe to be the first theoretical framework that articulates an integrated socio-cognitive account of curiosity that ties observable behaviors in peers to underlying curiosity states. We make a bipartite distinction between individual and interpersonal functions that contribute to curiosity, and multimodal behaviors that fulfill these functions. We validate the proposed framework by leveraging a longitudinal latent variable modeling approach. Findings confirm a positive predictive relationship between the latent variables of individual and interpersonal functions and curiosity, with the interpersonal functions exercising a comparatively stronger influence. Prominent behavioral realizations of these functions are also discovered in a data-driven manner. We instantiate the proposed theoretical framework in a set of strategies and tactics that can be incorporated into learning technologies to indicate, evoke, and scaffold curiosity. This work is a step towards designing learning technologies that can recognize and evoke moment-by-moment curiosity during learning in social contexts and towards a more complete multimodal learning analytics. The underlying rationale is applicable more generally for developing computer support for other metacognitive and socio-emotional skills.Comment: arXiv admin note: text overlap with arXiv:1704.0748

Using artificially generated pictures in customer-facing systems: an evaluation study with data-driven personas

We conduct two studies to evaluate the suitability of artificially generated facial pictures for use in a customer-facing system using data-driven personas. STUDY 1 investigates the quality of a sample of 1,000 artificially generated facial pictures. Obtaining 6,812 crowd judgments, we find that 90% of the images are rated medium quality or better. STUDY 2 examines the application of artificially generated facial pictures in data-driven personas using an experimental setting where the high-quality pictures are implemented in persona profiles. Based on 496 participants using 4 persona treatments (2 × 2 research design), findings of Bayesian analysis show that using the artificial pictures in persona profiles did not decrease the scores for Authenticity, Clarity, Empathy, and Willingness to Use of the data-driven personas.info:eu-repo/semantics/publishedVersio

Levels of naturalism in social neuroscience research

In order to understand ecologically meaningful social behaviors and their neural substrates in humans and other animals, researchers have been using a variety of social stimuli in the laboratory with a goal of extracting specific processes in real-life scenarios. However, certain stimuli may not be sufficiently effective at evoking typical social behaviors and neural responses. Here, we review empirical research employing different types of social stimuli by classifying them into five levels of naturalism. We describe the advantages and limitations while providing selected example studies for each level. We emphasize the important trade-off between experimental control and ecological validity across the five levels of naturalism. Taking advantage of newly emerging tools, such as real-time videos, virtual avatars, and wireless neural sampling techniques, researchers are now more than ever able to adopt social stimuli at a higher level of naturalism to better capture the dynamics and contingency of real-life social interaction