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A Framework For Effective Content Strategy Based On Heuristic Evaluation (Research Proposal)

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Nim Dvir, Information Studies PhD Student in The University at Albany

Research Proposal: A Framework For Effective Content Strategy Based On Heuristic

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A Framework For Effective Content Strategy Based On Heuristic Evaluation (Research Proposal)

  1. 1. Research Proposal: A Framework For Effective Content Strategy Based On Heuristic Evaluation Nim Dvir Department of Information Science, State University of New York at Albany INF 720
  2. 2. Research Question ● The impact of content strategy on user experience (UX) / consumer behavior in online commercial settings ● Develop a theoretical framework for systematic evaluation, optimization, and creation of effective content
  3. 3. Content Strategy ● The planning, development, and management of information—written or in other media ● Particularly common term in web development since the late 1990s ● A recognized field in UX design, but also draws interest from adjacent communities such as business analysis, and technical communication
  4. 4. Content User Heuristic Evaluation - a usability inspection method for computer software / information system that helps to identify usability problems in the user interface (UI). It specifically involves evaluators examining the interface and judging its compliance with recognized usability principles (the "heuristics")
  5. 5. Motivation Why Should You Care? ● Research opportunities ● Relevant to various domains ● All of us are ○ Content Consumers ○ Content Creators
  6. 6. Hypothesis ● Content influence UX ● Common attributes (heuristics) ● Methodology ● Generalisability (domains, demographics, Context) ● Not “magic”- part art, part science (and sometimes 100% science)
  7. 7. Objectives ● Develop a precise notion of effective content strategy ● Suggest generalized and overarching principles that can be applied across domains and applications (heuristics) ● Allow for quantitative evaluation and operational formulation ● Design a method—manual, semi-automated or fully automated—that can effectively improve a given content ● Aid in the larger task of understanding and optimizing UX and consumer behavior
  8. 8. Background HCI UX Content Strategy
  9. 9. Human-Computer Interaction (HCI) ● Focused on the interfaces between people (users) and computers ● Research in the field: ○ Observe the ways in which humans interact with computers ○ Design technologies that let humans interact with computers in novel ways
  10. 10. User Experience (UX) ● User experience (UX) refers to a person's emotions and attitudes about using a particular product, system or service ● Includes: ○ The practical, experiential, affective, meaningful and valuable aspects of human–computer interaction ○ User’s perceptions of system aspects such as utility, ease of use and efficiency
  11. 11. Gap Analysis ● Lack of Literature ● Current research is design-focused ● No agreement on ○ Definitions ○ Framework ○ Principles ○ Measurement ○ Criterion for success
  12. 12. Method Part I - Develop Theoretical Framework ● Unified Definitions - (IV - Heuristics, DV - Engagement) ● Conceptual Modeling - how users interact and consume content ● Heuristics - principles for engaging content strategy that can be used for assessment and automation ○ Similar to the practice of “Heuristics Evaluation” in design Part II - Test Heuristics & Model (Validation) Part III (Future Work) - Develop Systematic and Automatic Methods for Evaluation, Optimization, and Creation of Effective Content
  13. 13. Significance ● Methodological ○ Setting criteria for effective (engaging) content strategy ● Practical ○ Content is fundamental in work of scholars and practitioners in various domains (web development, digital marketing, education, and more)
  14. 14. Part I Developing a Theoretical Framework
  15. 15. Literature Review ● Comprehensive, multidisciplinary review of relevant literature across various disciplines (Marketing, Psychology, Education, Information Science and more) ● Synthesize knowledge ● Identify traditional and contemporary theories ● Refine terms ● Behavioral, Cognitive and Psychological principles
  16. 16. Exploratory Research - Qualitative and Quantitative ● Interviews ○ Content creators - scholars, influencers, and practitioners ○ Users ● Case Studies ● Content Analysis ● Social Media Analysis
  17. 17. Findings ● DV - “Engagement” ○ One basic process happening in many different contexts ● Definition ○ Attention over time / cognitive load (not always measurable) ● Conceptual Model ● IV - Heuristics
  18. 18. Part II Experiments (Test Heuristics)
  19. 19. 1. Visual Processing
  20. 20. 2. Ambiguity ● Processing Fluency ● Friction ● Norton, M. I., Frost, J. H., & Ariely, D. (2007). Less is more: The lure of ambiguity, or why familiarity breeds contempt. Journal of Personality and Social Psychology, 92(1),
  21. 21. ● A series of field experiments comparing two variants of a marketing webpage (n=27,083) ● Treatment - the amount of information provided online ● Effect - measured in a desired action called "conversion", In this case it's signing up for a newsletter). ● Less information actually resulted in more conversions consistently
  22. 22. 3. Familiarity and Fluency ● Show me what I like - Users engage with content they agree with ● NOT curious about opposing viewpoints ● Liberal Facebook and Conservative Facebook (WSJ) ○ Bakshy, Eytan; Messing, Solomon; Adamic, Lada, 2015, “Replication Data for: Exposure to Ideologically Diverse News and Opinion on Facebook”, http://dx.doi.org/10.7910/DVN/LDJ7MS, Harvard Dataverse, V2 ● Facebook shape people's perceptions and civic engagement - “News Feed” is not news ● Pew Research Center's survey - 44% of Americans rely on Facebook to get all their news
  23. 23. Distinctiveness Familiarity Sentiment A baseline prototype to operationalize the process using the Natural Language Toolkit (NLTK) and the WordNet Interface Goal: Automatic identification and replacement classify based on polarity (“negative,” “neutral,” or “positive”) using sentiment analysis, a natural language processing subfield analyze academic titles and abstracts to find synonyms with low frequency (out-of-context) Match disambiguated words to the IMDB keyword dictionary to find more familiar alternative selected words are placed back in the academic instances Derived a list of potential Sticky Words and revised academic titles using semantic and sentiment analysis Three key characteristics of Sticky Words Familiar Distinctive (unique, out of context) Emotional Value Sticky Memorability Engagement 4. Sticky Words
  24. 24. Part III Applications and Automation (Future Work)
  25. 25. Future Work ● Formalization ● Automate evaluation, creation and optimization of content strategy ● Natural language processing (NLP) and machine learning ● Eye Tracking ● further our research to various domains. ● Can our heuristics be used and the results could be generalized to other contexts? ● Can we identify and recommend different metrics in different contexts? ● Explore more domains / heuristics
  26. 26. Limitations ● User experience is considered subjective in nature ● Dynamic field - Constantly modified over changing usage circumstances, systems as well as the wider usage context in which they can be found. ● Relative to design, content is very specific to the context- can be argued that it is hard to generalize research results. ● Developing an automated method for evaluating and creating content is ambitious (complete formalization may not be achieved)
  27. 27. Recommendations ● Pivotal to consider content strategy in Business Information Systems ideation and design ● Practice critical thinking

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