Towards Automatic Persona Generation Using Social Media

An, Jisun; Cho, Hoyoun; Kwak, Haewoon; Hassen, Mohammed Ziyaad; Jansen, Bernard J.

doi:10.1109/w-ficloud.2016.51

Cited by 19 publications

(8 citation statements)

References 10 publications

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“…These individuals were tasked with assessing how representative the generated clusters were of real-life scenarios. An et al [2,3] applied CA (k-means) for persona generation, and observed that using individual-level data is expensive to collect and has concerns regarding privacy. However, the researchers did not address what kind of personas different algorithms would produce.…”

Section: Clusteringmentioning

confidence: 99%

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

et al. 2022

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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 criteria derived from ethical discussions in the computing community.

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Section: Clusteringmentioning

confidence: 99%

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

et al. 2022

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“…Automatic Persona Generation is the implementation of a methodology for quantitatively generating data-driven personas from online social media data [14]. Personas may be generated automatically, in real time, using very rich social media data that include timestamps such as YouTube, eliminating most of the labor associated with persona construction [15]. On the other hand, personas that are built from user data may be incomplete or incomprehensible, and therefore unusable as they are, requiring the designer to barge in and fill in the blanks [16].…”

Section: Related Workmentioning

confidence: 99%

Data-Assisted Persona Construction Using Social Media Data

Spiliotopoulos

Margaris

Vassilakis

2020

BDCC

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User experience design and subsequent usability evaluation can benefit from knowledge about user interaction, types, deployment settings and situations. Most of the time, the user type and generic requirements are given or can be obtained and used to model interaction during the design phase. The deployment settings and situations can be collected through the needfinding phase, either via user feedback or via the automatic analysis of existing data. Personas may be defined using the aforementioned information through user research analysis or data analysis. This work utilizes an approach to activate an accurate persona definition early in the design cycle, using topic detection to semantically enrich the data that are used to derive the persona details. This work uses Twitter data from a music event to extract information that can be used to assist persona creation. A user study in persona construction compares the topic modelling metadata to a traditional user collected data analysis for persona construction. The results show that the topic information-driven constructed personas are perceived as having better clarity, completeness and credibility. Additionally, the human users feel more attracted and similar to such personas. This work may be used to model personas and recommend suitable ones to designers of other products, such as advertisers, game designers and moviegoers.

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“…In persona profiles, customer attributes are displayed as 'people characteristics' that describe the core users or customers of the organization [50]. Showing human attributes instead of numbers, makes analytics data more approachable than numbers, especially for decision makers with limited analytics experience [1].…”

Section: Data-driven Personas and The Problem Of Transparencymentioning

confidence: 99%

Persona Transparency: Analyzing the Impact of Explanations on Perceptions of Data-Driven Personas

Salminen

Santos

Jung

et al. 2019

International Journal of Human–Computer Interaction

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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 transparencyi.e., explanations of how the information in data-driven personas was producedon 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.

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Towards Automatic Persona Generation Using Social Media

Cited by 19 publications

References 10 publications

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

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

Data-Assisted Persona Construction Using Social Media Data

Persona Transparency: Analyzing the Impact of Explanations on Perceptions of Data-Driven Personas

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