The trend towards the use of the Internet for health information purposes is rising. Utilization of various forms of social media has been a key interest in consumer health informatics (CHI). To reveal the information needs of autism-affected users, this study centers on the research of users’ interactions and information sharing within autism communities on social media. It aims to understand how autism-affected users utilize support groups on Facebook by applying natural language process (NLP) techniques to unstructured health data in social media. An interactive visualization method (pyLDAvis) was employed to evaluate produced models and visualize the inter-topic distance maps. The revealed topics (e.g., parenting, education, behavior traits) identify issues that individuals with autism were concerned about on a daily basis and how they addressed such concerns in the form of group communication. In addition to general social support, disease-specific information, collective coping strategies, and emotional support were provided as well by group members based on similar personal experiences. This study concluded that Latent Dirichlet Allocation (LDA) is feasible and appropriated to derive topics (focus) from messages posted to the autism support groups on Facebook. The revealed topics help healthcare professionals (content providers) understand autism from users’ perspectives and provide better patient communications.
This article describes how as internet technology continues to change and improve lives and societies worldwide, effective global information management becomes increasingly critical, and effective Internet information retrieval systems become more and more significant in providing Internet users worldwide with accurate and complete information. Search engine evaluation is an important research field as search engines directly determine the quality of information users' Internet searches. Relevance-decrease pattern/model plays an important role in search engine result evaluation. This research studies effective measurement of search results through investigating relevance-decrease patterns of search results from two popular search engines: Google and Bing. The findings can be applied to relevance-evaluation of search results from other information retrieval systems such as OPAC, can help make search engine evaluations more accurate and sound, and can provide global information management personnel with valuable insights.
Inducing more and higher-quality answers to questions is essential to sustainable development of Social Question-and-Answer (SQA) websites. Previous research has studied factors affecting question success and user motivation in answering questions, but how a question’s own characteristics affect the question’s answer outcome on SQA websites remains unknown. This study examines the impact of the characteristics of a question, namely readability, emotionality, additional descriptions, and question type, on the question’s answer outcome as measured by number of answers, average answer length, and number of “likes” received by answers to the question. Regression analyses reveal that readability, additional descriptions, and question type have significant impact on multiple measurements of answer outcome, while emotionality only affects the average answer length. This study provides insights to SQA website builders as they instruct users on question construction. It also provides insights to SQA website users on how to induce more and higher-quality answers to their questions.
New health-related concepts, terms, and topics emerge, and the meanings of existing terms and topics keep changing. This study investigated and explored the evolutions of the women’s health topic on Wikipedia. The creation time, page views data, page edits data, and text of historical versions of 207 women-health-related entries from 2010 to 2017 on Wikipedia were collected. Coding, subject analysis, descriptive and inferential statistical analysis, and Self-Organizing Map and n-gram approaches were employed to explore the characteristics and evolutions of the entries for the women’s health topic. The results show that the number of the women-health-related entries kept increasing from 2010 to 2017, and nearly half of them were related to the supports and protection of women’s health. The total number of page views of the investigated items increased from 2011 to 2013, but it decreased from 2013 to 2017, while the total number of page edits stayed stable from 2010 to 2017. Growing subjects were found during the investigated period, such as abuse and violence, and family planning and reproduction. However, the entries related to the economy and politics were diminishing. There was no association between the internal characteristic evolution and the external popularity evolution of the women’s health topic.
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