Coronavirus twitter analysis

Result

This repository is an analysis of the twitter dataset that provides how many times the #coronavirus was used in 2020 and from where the tweet was from. the results are stored in the viz folder as #coronavirus and #coronavirus_country using output redirection as specified in the tutorial below.

The #coronavirus contains the number of times the #coronavirus was tweeted which is preceded by what language was used to tweet for the first most used language.

The #coronavirus_country contains the number of times the #coronavirus was tweeted from a specific country and preceded by what country was the tweet from for the first top 10 country saved in the country directory under viz

Tutorial

You will scan all geotagged tweets sent in 2020 to monitor for the spread of the coronavirus on social media.

Due date: Sunday, 11 April.

This homework will require LOTs of computation time. I recommend that you have your code working by 21 Mar to ensure that you will have enough time to execute the code. No extensions will be granted for any reason.

You will continue to have assignments during the next few weeks, and so if you delay working on this homework, you will have some very heavy work loads ahead of you.

Learning Objectives:

work with large scale datasets
work with multilingual text
use the MapReduce divide-and-conquer paradigm to create parallel code

Background

Approximately 500 million tweets are sent everyday. Of those tweets, about 1% are geotagged. That is, the user's device includes location information about where the tweets were sent from. The lambda server's /data-fast/twitter\ 2020 folder contains all geotagged tweets that were sent in 2020. In total, there are about 1.1 billion tweets in this dataset. We can calculate the amount of disk space used by the dataset with the du command as follows:

$ du -h /data-fast/twitter\ 2020

The tweets are stored as follows. The tweets for each day are stored in a zip file geoTwitterYY-MM-DD.zip, and inside this zip file are 24 text files, one for each hour of the day. Each text file contains a single tweet per line in JSON format. JSON is a popular format for storing data that is closely related to python dictionaries.

Vim is able to open compressed zip files, and I encourage you to use vim to explore the dataset. For example, run the command

$ vim /data-fast/twitter\ 2020/geoTwitter20-01-01.zip

Or you can get a "pretty printed" interface with a command like

$ unzip -p /data-fast/twitter\ 2020/geoTwitter20-01-01.zip | head -n1 | python3 -m json.tool | vim -

You will follow the MapReduce procedure to analyze these tweets. MapReduce is a famous procedure for large scale parallel processing that is widely used in industry. It is a 3 step procedure summarized in the following image:

I have already done the partition step for you (by splitting up the tweets into one file per day). You will have to do the map and reduce steps.

Runtime:

The simplest and most common scenario is that the map procedure takes time O(n) and the reduce procedure takes time O(1). If you have p<<n processors, then the overall runtime will be O(n/p). This means that:

doubling the amount of data will cause the analysis to take twice as long;
doubling the number of processors will cause the analysis to take half as long;
if you want to add more data and keep the processing time the same, then you need to add a proportional number of processors.

More complex runtimes are possible. Merge sort over MapReduce is the classic example. Here, mapping is equivalent to sorting and so takes time O(n log n), and reducing is a call to the _reduce function that takes time O(n). But they are both rare in practice and require careful math to describe, so we will ignore them. In the merge sort example, it requires p=n processors just to reduce the runtime down to O(n)... that's a lot of additional computing power for very little gain, and so is impractical.

Background Tasks

Complete the following tasks to familiarize yourself with the sample code:

Fork the twitter_coronavirus repo and clone your fork onto the lambda server.
Mapping: The map.py file processes a single zip file of tweets. From the root directory of your clone, run the command
```
$ ./src/map.py --input_path=/data-fast/twitter\ 2020/geoTwitter20-02-16.zip
```
This command will take a few minutes to run as it is processing all of the tweets within the zip file. After the command finishes, you will now have a folder outputs that contains a file geoTwitter20-02-16.zip.lang. This is a file that contains JSON formatted information summarizing the tweets from 16 February.
Visualizing: The visualize.py file displays the output from running the map.py file. Run the command
```
$ ./src/visualize.py --input_path=outputs/geoTwitter20-02-16.zip.lang --key='#coronavirus'
```
This displays the total number of times the hashtag #coronavirus was used on 16 February in each of the languages supported by twitter. Now manually inspect the output of the .lang file using vim:
```
$ vim outputs/geoTwitter20-02-16.zip.lang
```
You should see that the file contains a dictionary of dictionaries. The outermost dictionary has languages as the keys, and the innermost dictionary has hashtags as the keys. The visualize.py file simply provides a nicer visualization of these dictionaries.
Reducing: The reduce.py file merges the outputs generated by the map.py file so that the combined files can be visualized. Generate a new output file by running the command
```
$ ./src/map.py --input_path=/data-fast/twitter\ 2020/geoTwitter20-02-17.zip
```
Then merge these output files together by running the command
```
$ ./src/reduce.py --input_paths outputs/geoTwitter20-02-16.zip.lang outputs/geoTwitter20-02-17.zip.lang --output_path=reduced.lang
```
Alternatively, you can use the glob to merge all output files with the command
```
$ ./src/reduce.py --input_paths outputs/geoTwitter*.lang --output_path=reduced.lang
```
Now you can visualize the reduced.lang file with the command
```
$ ./src/visualize.py --input_path=reduced.lang --key='#coronavirus'
```
and this displays the combined result.

Tasks

Complete the following tasks:

Modify the map.py file so that it tracks the usage of the hashtags on both a language and country level. This will require creating a variable counter_country similar to the variable counter_lang, and modifying this variable in the #search hashtags section of the code appropriately. The output of running map.py should be two files now, one that ends in .lang for the lanuage dictionary (same as before), and one that ends in .country for the country dictionary.

HINT: Most tweets contain a place key, which contains a dictionary with the country_code key. This is how you should lookup the country that a tweet was sent from. Some tweets, however, do not have a country_code key. This can happen, for example, if the tweet was sent from international waters or the international space station. Your code will have to be generic enough to handle edge cases similar to this without failing.
Once your map.py file has been modified to track results for each country, you should run the map file on all the tweets in the /data-fast/twitter\ 2020 folder. In order to do this, you should create a shell script run_maps.sh that loops over each file in the dataset and runs map.py on that file. Each call to map.py can take between minutes to hours to finish. (The exact runtime will depend on the server's load due to other students.) So you should use the nohup command to ensure the program continues to run after you disconnect and the & operator to ensure that all map.py commands run in parallel.
After your modified map.py has run on all the files, you should have a large number of files in your outputs folder. Use the reduce.py file to combine all of the .lang files into a single file, and all of the .country files into a different file. Then use the visualize.py file to count the total number of occurrences of each of the hashtags.

For each hashtag, you should create an output file in your repo using output redirection
```
$ ./src/visualize.py --input_path=PATH --key=HASHTAG | head > viz/HASHTAG
```
but replace PATH with the path to the output of your reduce.py file and HASHTAG is replaced with the hashtag you are analyzing.
Commit all of your code and visualization output files to your github repo and push the results to github. You must edit the README.md file to provide a brief explanation of your results. This explanation should be suitable for a future employer to look at while they are interviewing you to get a rough idea of what you accomplished. (And you should tell them about this in your interviews!)

afroCoderHanane / tc