Systematic Improvement of User Engagement with Academic Titles Using Computational Linguistics

Nim Dvir, 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. ‎
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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