Image Relevance on Websites and Readability (original) (raw)

References

1. Rughani, G., Hanlon, P., Corcoran, N., Mair, F.S.: The readability of general practice websites: a cross-sectional analysis of all general practice websites in Scotland. Br. J. Gen. Pract. 71(706), e391–e398 (2021)
   Article Google Scholar
2. Lundberg, A.: Web designers, don’t be afraid to use low-quality images in e-retail, unless you want to impress users: purchase intent and attitudes on product listing pages with varying product image quality (2021)
   Google Scholar
3. Bhavani, M., Narayana, V.A., Sreevani, G.: A novel approach for detecting near-duplicate web documents by considering images, text, size of the document and domain. In: Kumar, A., Mozar, S. (eds.) ICCCE 2020. LNEE, vol. 698, pp. 1355–1366. Springer, Singapore (2021). https://doi.org/10.1007/978-981-15-7961-5_123
   Chapter Google Scholar
4. Major, D., Gomes, D.: Web archives preserve our digital collective memory. In: The Past Web, pp. 11–19. Springer, Cham (2021).https://doi.org/10.1007/978-3-030-63291-5_2
5. Iglesias, A., Cobián, I., Campillo, A., Morato, J., Sánchez-Cuadrado, S.: Comp4Text checker: an automatic and visual evaluation tool to check the readability of Spanish web pages. In: Miesenberger, K., Manduchi, R., Covarrubias Rodriguez, M., Peňáz, P. (eds.) ICCHP 2020. LNCS, vol. 12376, pp. 258–265. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-58796-3_31
   Chapter Google Scholar
6. Ojha, P.K., Ismail, A., Srinivasan, K.K.: Perusal of readability with focus on web content understandability. J. King Saud Univ.-Comput. Inf. Sci. 33(1), 1–10 (2021)
   Google Scholar
7. Campillo, A., Morato, J., Maqueda, A.I., Sanchez-Cuadrado, S.: Readability of Spanish e-government information. In: 2020 15th Iberian Conference on Information Systems and Technologies (CISTI), pp. 1–4. IEEE, June 2020
   Google Scholar
8. Young, J., Dee, E.C., May, C.: Availability and readability of online patient information on osteosarcoma: assessment of pediatric hospital and National Cancer Institute-Designated Cancer Center (NCIDCC) Osteosarcoma Web Pages. JBJS Open Access 5(3) (2020)
   Google Scholar
9. Pantula, M., Kuppusamy, K.S.: A machine learning-based model to evaluate readability and assess grade level for the web pages. Comput. J. (2020)
   Google Scholar
10. Macedo-Rouet, M., et al.: How good is this page? Benefits and limits of prompting on adolescents’ evaluation of web information quality. Read. Res. Q. 54(3), 299–321 (2019)
    Article Google Scholar
11. Meade, M.J., Dreyer, C.W.: Web-based information on orthodontic clear aligners: a qualitative and readability assessment. Aust. Dent. J. 65(3), 225–232 (2020)
    Article Google Scholar
12. World Wide Web Consortium: Web content accessibility guidelines (WCAG) 2.0 (2008)
    Google Scholar
13. Murphy, J., Cameron, L.: Abstract. Br. J. Learn. Disabil. 36(4), 232–241 (2008)
    Google Scholar
14. Hawley, M.S., et al.: A voice-input voice-output communication aid for people with severe speech impairment. IEEE Trans. Neural Syst. Rehabil. Eng. 21(1), 23–31 (2012)
    Article Google Scholar
15. Moreland, K.: Why we use bad color maps and what you can do about it. Electron. Imaging 2016(16), 1–6 (2016)
    Article Google Scholar
16. Kondratova, I., Goldfarb, I.: Color your website: use of colors on the web. In: Aykin, N. (ed.) UI-HCII 2007. LNCS, vol. 4560, pp. 123–132. Springer, Heidelberg (2007). https://doi.org/10.1007/978-3-540-73289-1_16
    Chapter Google Scholar
17. Tavanapong, W., Li, W., Kim, K.-H.: Enhancing information quality for web pages. In: Chong, I. (ed.) ICOIN 2002. LNCS, vol. 2344, pp. 491–500. Springer, Heidelberg (2002). https://doi.org/10.1007/3-540-45801-8_47
    Chapter Google Scholar
18. Li, Z., Shi, S., Zhang, L.: Improving relevance judgment of web search results with image excerpts. In: Proceedings of the 17th International Conference on World Wide Web, pp. 21–30, April 2008
    Google Scholar
19. Maekawa, T., Hara, T., Nishio, S.: Image classification for mobile web browsing. In: Proceedings 15th International Conference on World Wide Web, pp. 43–52, May 2006
    Google Scholar
20. Chen, N., Zhou, Q.Y., Prasanna, V.: Understanding web images by object relation network. In: Proceedings of the 21st International Conference on World Wide Web, pp. 291–300, April 2012
    Google Scholar
21. Paek, S., Smith, J.R.: Detecting image purpose in world-wide web documents. In: Document Recognition V, vol. 3305, pp. 151–158. International Society for Optics and Photonics, April 1998
    Google Scholar
22. Miniukovich, A., Scaltritti, M., Sulpizio, S., De Angeli, A.: Guideline-based evaluation of web readability. In: Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems, pp. 1–12, May 2019
    Google Scholar
23. Yang, J., Li, Y., Gao, C., Zhang, Y.: Measuring the short text similarity based on semantic and syntactic information. Futur. Gener. Comput. Syst. 114, 169–180 (2021)
    Article Google Scholar
24. Yogish, D., Manjunath, T.N., Yogish, H.K., Hegadi, R.S.: Ranking top similar documents for user query based on normalized vector cosine similarity model. J. Comput. Theor. Nanosci. 17(9–10), 4531–4534 (2020)
    Article Google Scholar
25. Singh, R., Singh, S.: Text similarity measures in news articles by vector space model using NLP. J. Inst. Eng. (India): Ser. B 102(2), 329–338 (2020). https://doi.org/10.1007/s40031-020-00501-5
    Article Google Scholar
26. Dhiman, S., Singh, A.: Tesseract vs GOCR a comparative study. Int. J. Recent Technol. Eng. 2(4), 80 (2013)
    Google Scholar
27. Qurashi, A.W., Holmes, V., Johnson, A.P.: Document processing: methods for semantic text similarity analysis. In: 2020 International Conference on INnovations in Intelligent SysTems and Applications (INISTA), pp. 1–6. IEEE, August 2020
    Google Scholar
28. Huang, A.: Similarity measures for text document clustering. In: Proceedings of the Sixth New Zealand Computer Science Research Student Conference (NZCSRSC2008), Christchurch, New Zealand, vol. 4, pp. 9–56, April 2008
    Google Scholar

Download references