## 3 Methodology  
This section describes the step-by-step methodology that was followed for this research work. First of all, a relevant data set had to be selected. So, the dataset proposed in [114] that contains about 120,000 Tweet IDs of Tweets about exoskeletons, posted from 21 May 2017 to 21 May 2022, was used. The dataset in [114] was developed by using the Search Twitter _operator_ within RapidMiner [115] and utilizing the Twitter API's Advanced Search functionality [116]. RapidMiner is a data science platform that allows the design, development, and implementation of different algorithms in Big Data, Data Mining, Data Science, Artificial Intelligence, Machine Learning, and related disciplines. The Search Twitter _operator_ in RapidMiner operates by establishing a connection with the Twitter API while adhering to the rate limits for accessing Twitter data according to Twitter's Standard Search regulations. The Advanced Search characteristic of the Twitter API can be accessed by a user when they are logged into twitter.com. It allows users to search for Tweets based on time stamps, keywords, and a set of data filters, such as Tweets containing an exact phrase(s), Tweets containing any of the specified keywords, Tweets that exclude specific keywords, Tweets featuring a distinct hashtag, and Tweets in a particular language. After the collection of the Tweets using both these methodologies, driven by a keyword-based approach, the duplicate Tweets were removed in [114]. The dataset complies with the FAIR (Findability, Accessibility, Interoperability, and Reusability) principles for scientific data management [117]. The standard procedure for working with such Twitter datasets is to perform hydration of the Tweet IDs. However, this dataset was developed by the first author of this paper, so all the Tweets were already available for analysis. In addition to that, to include more recent Tweets for the analysis of this work, the same methodology for data collection as discussed in [114] was utilized to collect Tweets about exoskeletons from 22 May 2022 to 13 May 2023. 13 May 2023 was the most recent date at the time of data collection. Thereafter, the newly collected data was merged with the existing Tweets to develop a merged dataset for analysis. This dataset comprised 153,045 Tweets about exoskeletons, which were posted on Twitter between 21 May 2017, and 13 May 2023. These Tweets were posted by a total of 84,716 distinct users. In addition to the text of the Tweets, the data also contained characteristics associated with these Tweets, presented as different attributes. Table 1 summarizes the same [114]. \begin{table}
\begin{tabular}{l l} \hline \hline
**Attribute Name** & **Description** \\ \hline Row no. & Row number of the data \\ Id & ID of the Tweet \\ Created-At & Date and time when the Tweet was posted \\ From-User & Twitter username of the user who posted the Tweet \\ From-User-Id & Twitter User ID of the user who posted the Tweet \\ To-User & Twitter username of the user whose Tweet was replied to (if the Tweet was a reply) in the current Tweet \\ To-User-Id & Twitter user ID of the user whose Tweet was replied to (if the Tweet was a reply) in the current Tweet \\ Language & Language of the Tweet \\ Source & Source of the Tweet to determine if the Tweet was posted from an Android source, Twitter website, etc. \\ Text & Complete text of the Tweet, including embedded URLs \\ Geo-Location-Latitude & Geo-location (latitude) of the user posting the Tweet \\ Geo-Location-Longitude & Geo-location (longitude) of the user posting the Tweet \\ Retweet Count & Retweet count of the Tweet \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Description of the attributes in the dataset. Thereafter, the tweeting behavior per hour was analyzed. To perform this, the data present in the \"created_at\" attribute was analyzed. The data in this attribute contained both date and time information.