Understanding Actors and Evaluating Personae with Gaussian Embeddings

Understanding narrative content has become an increasingly popular topic. Nonetheless, research on identifying common types of narrative characters, or personae, is impeded by the lack of automatic and broad-coverage evaluation methods. We argue that computationally modeling actors provides benefits, including novel evaluation mechanisms for personae. Specifically, we propose two actor-modeling tasks, cast prediction and versatility ranking, which can capture complementary aspects of the relation between actors and the characters they portray. For an actor model, we present a technique for embedding actors, movies, character roles, genres, and descriptive keywords as Gaussian distributions and translation vectors, where the Gaussian variance corresponds to actors' versatility. Empirical results indicate that (1) the technique considerably outperforms TransE (Bordes et al. 2013) and ablation baselines and (2) automatically identified persona topics (Bamman, O'Connor, and Smith 2013) yield statistically significant improvements in both tasks, whereas simplistic persona descriptors including age and gender perform inconsistently, validating prior research.Comment: Accepted at AAAI 201

Persona transparency: analyzing the impact of explanations on perceptions of data-driven personas

Computational techniques are becoming more common in persona development. However, users of personas may question the information in persona profiles because they are unsure of how it was created. This problem is especially vexing for data-driven personas because their creation is an opaque algorithmic process. In this research, we analyze the effect of increased transparency–i.e., explanations of how the information in data-driven personas was produced–on user perceptions. We find that higher transparency through these explanations increases the perceived completeness and clarity of the personas. Contrary to our hypothesis, the perceived credibility of the personas decreases with the increased transparency, possibly due to the technical complexity of the persona profiles disrupting the facade of the personas being real people. This finding suggests that explaining the algorithmic process of data-driven persona creation involves a “transparency trade-off”. We also find that the gender of the persona affects the perceptions, with transparency increasing perceived completeness and empathy of the female persona, but not for the male persona. Therefore, transparency may specifically assist in the acceptance of female personas. We provide practical implication for persona creators regarding transparency in persona profiles.info:eu-repo/semantics/acceptedVersio

Tracing the Scenarios in Scenario-Based Product Design: a study to support scenario generation

Scenario-based design originates from the human-computer interaction and\ud software engineering disciplines, and continues to be adapted for product development. Product development differs from software development in the former’s more varied context of use, broader characteristics of users and more tangible solutions. The possible use of scenarios in product design is therefore broader and more challenging. Existing design methods that involve scenarios can be employed in many different stages of the product design process. However, there is no proficient overview that discusses a\ud scenario-based product design process in its full extent. The purposes of creating scenarios and the evolution of scenarios from their original design data are often not obvious, although the results from using scenarios are clearly visible. Therefore, this paper proposes to classify possible scenario uses with their purpose, characteristics and supporting design methods. The classification makes explicit different types of scenarios and their relation to one another. Furthermore, novel scenario uses can be referred or added to the classification to develop it in parallel with the scenario-based design\ud practice. Eventually, a scenario-based product design process could take inspiration for creating scenarios from the classification because it provides detailed characteristics of the scenario

What is an Analogue for the Semantic Web and Why is Having One Important?

This paper postulates that for the Semantic Web to grow and gain input from fields that will surely benefit it, it needs to develop an analogue that will help people not only understand what it is, but what the potential opportunities are that are enabled by these new protocols. The model proposed in the paper takes the way that Web interaction has been framed as a baseline to inform a similar analogue for the Semantic Web. While the Web has been represented as a Page + Links, the paper presents the argument that the Semantic Web can be conceptualized as a Notebook + Memex. The argument considers how this model also presents new challenges for fundamental human interaction with computing, and that hypertext models have much to contribute to this new understanding for distributed information systems

Big Data, Small Personas : How Algorithms Shape the Demographic Representation of Data-Driven User Segments

Derived from the notion of algorithmic bias, it is possible that creating user segments such as personas from data results in over- or under-representing certain segments (FAIRNESS), does not properly represent the diversity of the user populations (DIVERSITY), or produces inconsistent results when hyperparameters are changed (CONSISTENCY). Collecting user data on 363M video views from a global news and media organization, we compare personas created from this data using different algorithms. Results indicate that the algorithms fall into two groups: those that generate personas with low diversity–high fairness and those that generate personas with high diversity–low fairness. The algorithms that rank high on diversity tend to rank low on fairness (Spearman's correlation: −0.83). The algorithm that best balances diversity, fairness, and consistency is Spectral Embedding. The results imply that the choice of algorithm is a crucial step in data-driven user segmentation, because the algorithm fundamentally impacts the demographic attributes of the generated personas and thus influences how decision makers view the user population. The results have implications for algorithmic bias in user segmentation and creating user segments that not only consider commercial segmentation criteria but also consider criteria derived from ethical discussions in the computing community.©2022, Mary Ann Liebert, Inc., publishers.fi=vertaisarvioitu|en=peerReviewed