Systematic Improvement of User Engagement with Academic Titles Using Computational Linguistics
Nim Dvir
, Ruti Gafni
, Ruti Gafni
InSITE 2019
• 2019
• pp. 501-512
Aim/Purpose: This paper describes a novel approach to systematically improve information interactions based solely on its wording.
Background: Providing users with information in a form and format that maximizes its effectiveness is a research question of critical importance. Given the growing competition for users’ attention and interest, it is agreed that digital content must engage. However, there are no clear methods or frameworks for evaluation, optimization and creation of such engaging content.
Methodology: Following an interdisciplinary literature review, we recognized three key attributes of words that drive user engagement: (1) Novelty (2) Familiarity (3) Emotionality. Based on these attributes, we developed a model to systematically improve a given content using computational linguistics, natural language processing (NLP) and text analysis (word frequency, sentiment analysis and lexical substitution). We conducted a pilot study (n=216) in which the model was used to formalize evaluation and optimization of academic titles. A between-group design (A/B testing) was used to compare responses to the original and modified (treatment) titles. Data was collected for selection and evaluation (User Engagement Scale).
Contribution: The pilot results suggest that user engagement with digital information is fostered by, and perhaps dependent upon, the wording being used. They also provide empirical support that engaging content can be systematically evaluated and produced.
Findings: The preliminary results show that the modified (treatment) titles had significantly higher scores for information use and user engagement (selection and evaluation).
Recommendations for Practitioners: We propose that computational linguistics is a useful approach for optimizing information interactions. The empirically based insights can inform the development of digital content strategies, thereby improving the success of information interactions.
Recommendations for Researchers: By understanding and operationalizing content strategy and engagement, we can begin to focus efforts on designing interfaces which engage users with features appropriate to the task and context of their interactions. This study will benefit the information science field by enabling researchers and practitioners alike to understand the dynamic relationship between users, computer applications and tasks, how to assess whether engagement is taking place and how to design interfaces that engage users.
Impact on Society: This research can be used as an important starting point for understanding the phenomenon of digital information interactions and the factors that promote and facilitates them. It can also aid in the development of a broad framework for systematic evaluation, optimization, and creation of effective digital content.
Future Research: Moving forward, the validity, reliability and generalizability of our model should be tested in various contexts. In future research, we propose to include additional linguistic factors and develop more sophisticated interaction measures.
Background: Providing users with information in a form and format that maximizes its effectiveness is a research question of critical importance. Given the growing competition for users’ attention and interest, it is agreed that digital content must engage. However, there are no clear methods or frameworks for evaluation, optimization and creation of such engaging content.
Methodology: Following an interdisciplinary literature review, we recognized three key attributes of words that drive user engagement: (1) Novelty (2) Familiarity (3) Emotionality. Based on these attributes, we developed a model to systematically improve a given content using computational linguistics, natural language processing (NLP) and text analysis (word frequency, sentiment analysis and lexical substitution). We conducted a pilot study (n=216) in which the model was used to formalize evaluation and optimization of academic titles. A between-group design (A/B testing) was used to compare responses to the original and modified (treatment) titles. Data was collected for selection and evaluation (User Engagement Scale).
Contribution: The pilot results suggest that user engagement with digital information is fostered by, and perhaps dependent upon, the wording being used. They also provide empirical support that engaging content can be systematically evaluated and produced.
Findings: The preliminary results show that the modified (treatment) titles had significantly higher scores for information use and user engagement (selection and evaluation).
Recommendations for Practitioners: We propose that computational linguistics is a useful approach for optimizing information interactions. The empirically based insights can inform the development of digital content strategies, thereby improving the success of information interactions.
Recommendations for Researchers: By understanding and operationalizing content strategy and engagement, we can begin to focus efforts on designing interfaces which engage users with features appropriate to the task and context of their interactions. This study will benefit the information science field by enabling researchers and practitioners alike to understand the dynamic relationship between users, computer applications and tasks, how to assess whether engagement is taking place and how to design interfaces that engage users.
Impact on Society: This research can be used as an important starting point for understanding the phenomenon of digital information interactions and the factors that promote and facilitates them. It can also aid in the development of a broad framework for systematic evaluation, optimization, and creation of effective digital content.
Future Research: Moving forward, the validity, reliability and generalizability of our model should be tested in various contexts. In future research, we propose to include additional linguistic factors and develop more sophisticated interaction measures.
information behavior, text analysis, computational linguistics, information interaction, user experience (UX), knowledge acquisition, decision-making, user engagement, content strategy, digital nudging
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