How to create a Python script for automating social media analytics and reporting?

Creating an automation script for social media analytics and reporting involves various steps. Before we begin, you should have a basic understanding of Python. Furthermore, you should have your accounts set up on social media platforms for which you want to automate the analytics.

For this example, we will use the Twitter API and a Python library called Tweepy for Twitter API.

Step 1: Set up Twitter Developer Account:

Twitter has its own set of APIs to access its vast amount of data. In order to access these, you need to have a developer account.

- Sign up for a Twitter Developer account if you do not have one. 
- Create an application in the developer console.

Here’s what you need to do to create one:
- Go to the Twitter Developer site (https://developer.twitter.com/) and sign in with the Twitter account you want to link with your app.
- Click on “Create an app” button. You will have to fill out an application to get your API key. Once that’s done, you get the API key immediately.

Step 2: Install Python and Tweepy:

We recommend installing Python from the official site https://www.python.org/ if you do not have it installed and Python's package installer pip.

You can install Tweepy using pip:

pip install tweepy

Step 3: Accessing Twitter Data:

Once you have your API keys, you can start accessing Twitter data. Let’s see how:

import tweepy

# assign the values accordingly
consumer_key = "your consumer key"
consumer_secret = "your consumer secret key"
access_token = "your access token"
access_token_secret = "your secret access token"

# authorization of consumer key and consumer secret
auth = tweepy.OAuthHandler(consumer_key, consumer_secret)

# set access to user's access key and access secret 
auth.set_access_token(access_token, access_token_secret)

# calling the api 
api = tweepy.API(auth)

With this, we’re now ready to start pulling some data from Twitter.

Step 4: Analyzing Twitter data:

Once you have the data, you can start analyzing it the way you want. Here is an example to get the 5 most recent tweets from the official CNN Twitter account:

# the ID of the user
id = "CNN"

# fetching the user
user = api.get_user(id)

# fetching the statuses_count attribute
statuses_count = user.statuses_count

# printing the statuses_count
print("The number of statuses this user has posted: " + str(statuses_count))

# fetching the count attribute
count = 5

# fetching the status
statuses = api.user_timeline(id, count = count)

# printing the statuses
for status in statuses:
    print(status.text)

Step 5: Final step would be to format these collected data into a report. This would involve saving user data, tweet content, retweet count, favourite count etc. into a CSV file:

import csv

# a list of "1 to count" numbers
numbers = [x for x in range(1, count+1)]

# a list of all tweets
tweets = [status.text for status in statuses]

# we will iterate over each status and we will fetch the text attribute.
for status in statuses:
    tweets.append(status.text)

header = ['Number', 'Tweet']

# creating a csv file
with open('tweets.csv', 'w', encoding='UTF8') as f:
    writer = csv.writer(f)

    # write the header
    writer.writerow(header)

    # write the data
    for x in range(count):
        writer.writerow([numbers[x], tweets[x]])

This will give you a CSV file, named tweets.csv, which will store the tweets.

This is a basic example, and you can build on this to fit your specific needs. For example, you can apply Natural Language Processing techniques to clean the tweets and then use sentiment analysis to see how people are reacting to different subjects.

For other social media platforms, such as Facebook or Instagram, you would need to use their respective APIs, like Facebook Graph API and Instagram Graph API. The approach will be similar - you pull data, process it, and build a report around it. Please make sure to adhere to the usage policies of respective APIs to avoid any legal issues.