Bridging Hard and Soft Logic: The Emergence of Data-Driven, Algorithmically-Created Personas

With user experience (UX) research and market analysis, the development of data-driven, algorithmically-created personas stands at the forefront, marking a significant leap in understanding diverse population segments. These algorithmically-created personas, crafted through a synergy of big data, machine learning (ML), and artificial intelligence (AI), are revolutionizing our approach to understanding audiences, customers, and users by…… Continue reading Bridging Hard and Soft Logic: The Emergence of Data-Driven, Algorithmically-Created Personas

The Need for Formal Standards for Persona Creation

Personas are invaluable tools in content creation that help creators and editors better understand their target audiences. In the business world, personas are valuable tools that help marketers and executives better understand their target customers. In the user experience (UX) design world, personas are invaluable tools that help designers and developers better understand their target…… Continue reading The Need for Formal Standards for Persona Creation

What Information to Include in a Persona Profile?

Algorithmically generated personas have emerged as quintessential tools for understanding audiences, customers, and users, bridging data analytics and human-centric experiences. These algorithmically-generated personas (APGs) are at the intersection of user experience research, marketing strategies, and product development, and APGs are realistically crafted identities that mirror the varied facets of a target audience, customer, or user…… Continue reading What Information to Include in a Persona Profile?

How Do Users Perceive Deepfake Personas? Investigating the Deepfake User Perception and Its Implications for Human-Computer Interaction

Although deepfakes have a negative connotation in human-computer interaction (HCI) due to their risks, they also involve many opportunities, such as communicating user needs in the form of a “living, talking” deepfake persona. To scope and better understand these opportunities, we present a qualitative analysis of 46 participants’ think-aloud transcripts based on interacting with deepfake…… Continue reading How Do Users Perceive Deepfake Personas? Investigating the Deepfake User Perception and Its Implications for Human-Computer Interaction

StoryOptimizer – Leveraging AI for better cross platform social media content creation

If your organization does cross-platform posting (i.e., posts the same or similar content on multiple social media platforms). As part of our Acua system, we’ve developed a feature that takes a generic post and automatically optimizes it for 6 social media platforms. See below. We have already tested the feature in a 900-person user study,…… Continue reading StoryOptimizer – Leveraging AI for better cross platform social media content creation

SegmentSizeEstimator estimates the segment size based on language, country, gender, age-range, and topical interest

SegmentSizeEstimator estimates the segment size based on language, country, gender, age range, and topical interest. Moreover, it finds the most likable topical interest based on the given story and provides related segment sizes. This is powered by Facebook Ads API and (if available) Twitter Ads API. Give it a try! SegmentSizeEstimator Read more! Jansen, B.…… Continue reading SegmentSizeEstimator estimates the segment size based on language, country, gender, age-range, and topical interest

Text2Toxicity predicts a text’s toxicity

Text2Toxicity predicts a text’s toxicity. The tool is based on the machine learning classifier reported in the research article mentioned below. Give it a try! Text2Toxicity Salminen, J., Hopf, M., Chowdhury, S. A., Jung, S., Almerekhi, H., & Jansen, B. J. (2020). Developing an online hate classifier for multiple social media platforms. Human-Centric Computing and Information Sciences,…… Continue reading Text2Toxicity predicts a text’s toxicity

GAN2Name provides the most likely names for the given demographic

GAN2Name provides the most likely names for the given demographic. “GAN” comes from Gender, Age, and Nationality. Give it a try! GAN2Name Read more! ​Jung, S.G., Salminen, J., and Jansen, B. J. (2021) All About the Name: Assigning Demographically Appropriate Names to Data-Driven Entities. 54th Annual Hawaii International Conference on System Sciences (HICSS 2021) Koloa, Hawaii, United States. 5-8…… Continue reading GAN2Name provides the most likely names for the given demographic

Name2GAN Predict a name’s most likely demographics

Name2GAN is a service of Acua used to predict a name’s most likely demographics. “GAN” comes from Gender, Age, Nationality. Give it a try! Name2GAN ​Read more about the service! Jung, S.G., Salminen, J., and Jansen, B. J. (2021) All About the Name: Assigning Demographically Appropriate Names to Data-Driven Entities. 54th Annual Hawaii International Conference on System…… Continue reading Name2GAN Predict a name’s most likely demographics

Is Death Only the Beginning? How People Mourn Artificial Characters in Social Media

We analyze the audience response to the death of narrative-driven fictitious characters with predetermined fates, whether part of a virtual or cinematic story, and specifically from video games and TV series. Our aim is to contribute to the studies of identification and empathy with fictitious characters in media, as well as to close the research…… Continue reading Is Death Only the Beginning? How People Mourn Artificial Characters in Social Media