Journal of Medical Internet Research - International Scientific Journal for Medical Research, Information and Communication on the Internet #Preprint #PeerReviewMe: Warning: This is a unreviewed preprint. Readers are warned that the document has not been peer-reviewed by expert/patient reviewers or an academic editor, may contain misleading claims, and is likely to undergo changes before final publication, if accepted, or may have been rejected/withdrawn. Readers with interest and expertise are encouraged to sign up as peer-reviewer, if the paper is within an open peer-review period. Please cite this preprint only for review purposes or for grant applications and CVs (if you are the author). Background: Social media have been increasingly adopted by health agencies and professionals to disseminate information, interact with public, and understand public opinion. Among them, the Centers for Disease Control and Prevention (CDC) is arguably one of the first government health agencies to adopt social media during health emergencies and crisis. It had been active on Twitter during the 2016 Zika epidemic that caused 5,168 domestic cases in the United States. Objective: This study aims to quantify the temporal variabilities in CDC’s tweeting activities over the course of Zika epidemic, public engagement in these CDC-initiated tweets (i.e., retweets and replies), and Zika case counts. It then compares the patterns of these three sets of data to identify the discrepancy and consistency among actual domestic Zika case counts, CDC response on Twitter, and public engagement in this topic. Methods: All of the CDC-initiated tweets published in 2016 with corresponding retweets and replies were collected from 67 CDC-associated Twitter accounts. Both univariate (ARIMA model) and multivariate time series analyses (CCF, mutual Shannon information entropy, ARIMAX model, Granger test) were performed in each quarter of 2016 for domestic Zika case counts, CDC tweeting activities, and public engagement in the CDC-initiated tweets. Results: CDC sent out more than 84% of its Zika tweets in the first quarter of 2016 when Zika case counts were low in the 50 U.S. states and territories. While Zika case counts increased dramatically in the second and the third quarters, CDC efforts on Twitter plunged. Time series of public engagement in the CDC-initiated tweets generally differed among quarters and from that of original CDC tweets based on ARIMA model parameters. Original CDC tweets and public engagement both had highest mutual information with Zika case counts in the 2nd quarter. Public engagement in the original CDC tweets was also substantially influenced by and preceded actual Zika epidemic. Conclusions: There were substantial discrepancies among CDC’s original tweets regarding Zika, public engagement in these tweets, and actual Zika epidemic. The patterns of these discrepancies also varied between different quarters in 2016. We discovered that although CDC was very effective in early warning of Zika in the first quarter of 2016, it failed to respond in a timely manner on Twitter during the rest of the Zika epidemic. Public engagement in CDC’s original tweets served as a more prominent predictor of actual Zika epidemic than number of CDC’s original tweets later in the year.

Background: Social media has become a major resource for observing and understanding public opinions using infodemiology and infoveillance methods, especially during emergencies such as disease outbreaks. For public health agencies, understanding the driving forces of web-based discussions will help deliver more effective and efficient information to general users on social media and the web. Objective: The study aimed to identify the major contributors that drove overall Zika-related tweeting dynamics during the 2016 epidemic. In total, 3 hypothetical drivers were proposed: (1) the underlying Zika epidemic quantified as a time series of case counts; (2) sporadic but critical real-world events such as the 2016 Rio Olympics and World Health Organization’s Public Health Emergency of International Concern (PHEIC) announcement, and (3) a few influential users’ tweeting activities. Methods: All tweets and retweets (RTs) containing the keyword Zika posted in 2016 were collected via the Gnip application programming interface (API). We developed an analytical pipeline, EventPeriscope, to identify co-occurring trending events with Zika and quantify the strength of these events. We also retrieved Zika case data and identified the top influencers of the Zika discussion on Twitter. The influence of 3 potential drivers was examined via a multivariate time series analysis, signal processing, a content analysis, and text mining techniques. Results: Zika-related tweeting dynamics were not significantly correlated with the underlying Zika epidemic in the United States in any of the four quarters in 2016 nor in the entire year. Instead, peaks of Zika-related tweeting activity were strongly associated with a few critical real-world events, both planned, such as the Rio Olympics, and unplanned, such as the PHEIC announcement. The Rio Olympics was mentioned in >15% of all Zika-related tweets and PHEIC occurred in 27% of Zika-related tweets around their respective peaks. In addition, the overall tweeting dynamics of the top 100 most actively tweeting users on the Zika topic, the top 100 users receiving most RTs, and the top 100 users mentioned were the most highly correlated to and preceded the overall tweeting dynamics, making these groups of users the potential drivers of tweeting dynamics. The top 100 users who retweeted the most were not critical in driving the overall tweeting dynamics. There were very few overlaps among these different groups of potentially influential users. Conclusions: Using our proposed analytical workflow, EventPeriscope, we identified that Zika discussion dynamics on Twitter were decoupled from the actual disease epidemic in the United States but were closely related to and highly influenced by certain sporadic real-world events as well as by a few influential users. This study provided a methodology framework and insights to better understand the driving forces of web-based public discourse during health emergencies. Therefore, health agencies could deliver more effective and efficient web-based communications in emerging crises.

Actionability is a mode of revealing actionable knowledge in the form of Action Rules from large datasets. Action rule imparts in the form of recommendations as how a data object can change from one value to another more desirable value. The towering production of data in the recent years, due to increased usage of web, social media and IoT, has led to the age of big data. Also, abundant usage of cloud storages and cloud based services causes the data to be spread around the globe. This requires more time and space for a single computer to cope with such widespread data. Ecosystems like Hadoop MapReduce, Spark have been introduced to store, process and retrieve back the data efficiently in a distributed fashion. Data mining finds substantial improvements over such distributed frameworks to process huge volume of data and acquire knowledge from them in a short span of time. In this paper, we present an approach SARGS: Specific Action Rule discovery based on Grabbing Strategy, to build more specific Action Rules using Apache Spark framework and evaluate the results with our previous Hadoop MapReduce system (MR-Random Forest Algorithm for Distributed Action Rules Discovery). Also, we propose a novel approach to distribute data in a distributed environment to get more optimal Action Rules and upgraded ARoGS algorithm to get more specific Action Rules.

This research proposes a novel strategy for constructing a knowledge-based recommender system (RS) based on both structured data and unstructured text data. We present its application to improve the services of heavy equipment repair companies to better adjust to their customers’ needs. The ultimate outcome of this work is a visualized web-based interactive recommendation dashboard that shows options that are predicted to improve the customer loyalty metric, known as Net Promoter Score (NPS). We also present a number of techniques aiming to improve the performance of action rule mining by allowing to have convenient periodic updates of the system’s knowledge base. We describe the preprocessing-based and distributed-processing-based method and present the results of testing them for performance within the RS framework. The proposed modifications for the actionable knowledge miner were implemented and compared with the original method in terms of the mining results/times and generated recommendations. Preprocessing-based methods decreased mining by 10–20×, while distributed mining implementation decreased mining timesby 300–400×, with negligible knowledge loss. The article concludes with the future directions for the scalability of the NPS recommender system and remaining challenges in its big data processing.