## Appendix A  
```
Input: Dataset Output: New attribute for the time of each tweet (24-h format) Temp = [] File path Read data as dataframe For from 0 to n do  obtain timestamp of the Tweet ('created_at' column)  local variable x = 0  hr, min, sec (use RegEx) Function myBins: parameter hr and min  convert hour to int  construct using x (bin 1-24)  x = myBins(hr, min) End of Function End of for loop  temp \(\leftarrow\) append(x) Add new attribute to the dataset Save the data
```  
**Algorithm A1: Extraction of the Time of Each Tweet**  
```
Input: Dataset Output: Mean and Median Character Count of Tweets per hour (24-h format) Bin 1-24 = [], med = [], mean = [] File path Read data as dataframe function to find the mean(arr): if length(arr) is 0 then  return 0 Else  calculate mean of array  return mean End of Function Function median(arr): if length of arr is 0 then  return 0 Else  calculate median of array  return median End of Function for i from 0 to n do  ith element character_count (Tweet)  mean(arr)  median(arr) End of for loop  list \(\leftarrow\) append (mean, median)  Set the field in the dataset to a new series from the list Add new attributes to the dataset Save the data
```  
**Algorithm A2: Median and Mean Character Count of Tweets per Hour**```
Input: Dataset Output: Courts of Hashtags and User Mentions used per hour (24-h format) File Path Read data as dataframe hashtagCount = 0, UserMentionCount = 0 for i from 0 to n do  string \(\leftarrow\) convert to string(dataset['text'[i]]  find length of the string for j from 0 to x - 1 do if string[j] equals to ''then  increment hashtagCount \(\leftarrow\) append count if string[j] equals to ''then  increment UserMentionCount \(\leftarrow\) append count  UserMentionCount \(\leftarrow\) append count End of for loop hashtagCount \(\leftarrow\) set hashtag_count column in dataset UserMentionCount \(\leftarrow\) set userMentionCount in dataset timeOHHashtags[] timeOfUserMentions[] for i from 0 to n do  set count to zero for k from 0 to n do if dataset['bin'[j] equals i then  increment count by dataset['hashtag_count'][k]  count \(\leftarrow\) set timeOfUserMentions [i - 1] End of for loop for i from 0 to n do  set count to zero for k from 0 to n do if dataset['bin'[j]] equals i then  increment count by dataset['usermention_count'][k]  count \(\leftarrow\) set timeofUserMentions [i - 1] End of for loop Add new attributes to the dataset Save the data
```

**Algorithm A3: Number of Hashtags and User Mentions per Hour (24-h Format)**

```
Input: Dataset Output: Correlations (using Pearson's correlation coefficient) between the investigated characteristics, Multiple Linear Regression Equation, and Performance Characteristics File Path Read data as dataframe Create a heatmap Xtick_loc = retrieve the \(x\)-axis tick location Xtick_labels = retrieve the \(x\)-axis tick labels Stat_1 \(\leftarrow\) Pearsons Correlation (Tweets per hour, characters (mean) used in the Tweets per hour) Stat_2 \(\leftarrow\) Pearsons Correlation (Tweets per hour, characters (median) used in the Tweets per hour) Stat_3 \(\leftarrow\) Pearsons Correlation (Tweets per hour, number of hashtags used per hour) Stat_4 \(\leftarrow\) Pearsons Correlation (number of Tweets per hour, number of user mentions used per hour) Xl = Tweets per hour, characters (median) used in the Tweets per hour, number of hashtags used per hour mentions used per hour Yl = number of Tweets per hour Initialize and fit the linear regression model (using sklearn_linear_model) Print Intercept and Coefficient using model_intercept_ and model_coef_ Generate X_train, X_test, y_train, y_test using split ratio 80:20
```
**Algorithm A4: Cont.