Leveraging Python to Analyze Firmographic and Social Media Data for Optimal Juror Selection

*This article spun out of a conversation I had with a good friend, who is a lawyer. They are diving deeper into the data realm and raised some interesting problems they’re looking to solve, leveraging data.

**I am not a lawyer, and this is not legal advice**

Introduction:

Selecting the right juror for a case is crucial for any lawyer. In today’s digital age, firmographic data and social media data can provide valuable insights into a juror’s background and personality. This article will guide you through the process of using Python to analyze this data, helping you make more informed decisions during juror selection.

Section 1: Gathering Firmographic and Social Media Data

1.1 Firmographic Data

Firmographic data includes information about a juror’s employer, job title, industry, company size, and location. You can collect this data from various public and subscription-based sources, such as company websites and online databases like LinkedIn. Once you’ve collected this data, you can store it in a CSV or Excel file for further analysis.

As of September 2021, LinkedIn has restricted access to its APIs, limiting the availability of data for non-partner developers. However, you can still access some basic profile information by using the LinkedIn API. First, you’ll need to create an application on the LinkedIn Developer portal and obtain the required API keys and access tokens.

To access the LinkedIn API, follow these steps:

1. Visit the LinkedIn Developer portal (https://www.linkedin.com/developers/) and sign in with your LinkedIn account.
2. Click on “Create app” and fill in the necessary details to create a new application.
3. After creating the application, go to the “Auth” tab in your app settings, and take note of your “Client ID” and “Client Secret”.
4. Set up the required OAuth 2.0 authentication flow to obtain an access token. You can use a library like requests-oauthlib to simplify the process.

import requests

# Set access token
access_token = 'your_access_token'

# Retrieve basic profile information
url = 'https://api.linkedin.com/v2/me?projection=(id,firstName,lastName,profilePicture(displayImage~:playableStreams))'
headers = {'Authorization': f'Bearer {access_token}'}
response = requests.get(url, headers=headers)
profile_data = response.json()

# Extract data from the response
first_name = profile_data['firstName']['localized']['en_US']
last_name = profile_data['lastName']['localized']['en_US']
profile_picture_url = profile_data['profilePicture']['displayImage~']['elements'][0]['identifiers'][0]['identifier']

print(f'First Name: {first_name}')
print(f'Last Name: {last_name}')
print(f'Profile Picture URL: {profile_picture_url}')

Keep in mind that the LinkedIn API provides limited access to user data, and you may need to apply for LinkedIn Partner status to access more comprehensive profile information, including firmographic data.

Please note that the example provided above assumes that you have already completed the OAuth 2.0 authentication process and have a valid access token. Additionally, scraping LinkedIn data without explicit permission is against their terms of service and can result in account restrictions or bans.

1.2 Social Media Data

Social media data comprises information derived from a juror’s social media profiles, such as Facebook, Twitter, and Instagram. This data can include likes, shares, comments, and posts, which can reveal a juror’s interests, political affiliations, and opinions on various topics. You can collect this data using APIs provided by the respective platforms or web scraping tools like Beautiful Soup and Scrapy.

1.3 Collecting Social Media Data

To collect social media data, you can use APIs provided by popular social media platforms like Facebook, Twitter, and Instagram. You’ll need to register an application and obtain API keys for each platform you wish to collect data from.

1.4 Twitter API

First, install the Tweepy library, which simplifies accessing the Twitter API.

pip install tweepy

Next, use the following code snippet to retrieve a user’s tweets:

import tweepy

# Set API keys and tokens
consumer_key = 'your_consumer_key'
consumer_secret = 'your_consumer_secret'
access_token = 'your_access_token'
access_token_secret = 'your_access_token_secret'

# Authenticate to the Twitter API
auth = tweepy.OAuthHandler(consumer_key, consumer_secret)
auth.set_access_token(access_token, access_token_secret)
api = tweepy.API(auth)

# Retrieve tweets from a user's timeline
username = 'target_username'
tweets = api.user_timeline(screen_name=username, count=200, tweet_mode='extended')

# Extract text from tweets
tweet_texts = [tweet.full_text for tweet in tweets]

1.5 Facebook API

pip install requests

Next, use the following code snippet to retrieve a user’s posts:

import requests

# Set access token
access_token = 'your_access_token'

# Retrieve posts from a user's page
page_id = 'target_page_id'
url = f'https://graph.facebook.com/v12.0/{page_id}/posts?access_token={access_token}'
response = requests.get(url)
posts_data = response.json()

# Extract text from posts
post_texts = [post['message'] for post in posts_data['data']]

1.6 Instagram API

First, install the instaloader library, which simplifies accessing the Instagram API.

pip install instaloader

Next, use the following code snippet to retrieve a user’s posts:

import instaloader

# Create an Instaloader instance
L = instaloader.Instaloader()

# Retrieve posts from a user's profile
username = 'target_username'
profile = instaloader.Profile.from_username(L.context, username)
posts = profile.get_posts()

# Extract text from posts
post_texts = [post.caption for post in posts]

Section 2: Analyzing Firmographic and Social Media Data in Python

2.1 Importing Data

First, import the necessary libraries and read your data from the CSV or Excel file.

import pandas as pd
import numpy as np
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.metrics.pairwise import cosine_similarity

# Read data from CSV file
data = pd.read_csv('juror_data.csv')

2.2 Feature Engineering

Next, create new features that will help you analyze the data. For example, you can create a “political_affiliation” feature based on the juror’s social media posts and likes.

def detect_political_affiliation(posts):
    # Define keywords related to different political affiliations
    conservative_keywords = ['republican', 'conservative', 'gop']
    liberal_keywords = ['democrat', 'liberal', 'progressive']

    conservative_score = sum([keyword in posts for keyword in conservative_keywords])
    liberal_score = sum([keyword in posts for keyword in liberal_keywords])

    if conservative_score > liberal_score:
        return 'Conservative'
    elif liberal_score > conservative_score:
        return 'Liberal'
    else:
        return 'Neutral'

data['political_affiliation'] = data['social_media_posts'].apply(detect_political_affiliation)

2.3 Analyzing Text Data

To analyze the text data from social media posts, you can use the TF-IDF (Term Frequency-Inverse Document Frequency) method to find relevant keywords and topics. Then, you can calculate the cosine similarity between each juror’s posts and the case’s main topics to find the jurors with the most similar interests.

# Define the main topics of the case
case_topics = "topic1, topic2, topic3"

# Create a TfidfVectorizer instance
vectorizer = TfidfVectorizer(stop_words='english')

# Fit the vectorizer to the juror's social media posts
tfidf_matrix = vectorizer.fit_transform(data['social_media_posts'])

# Calculate the cosine similarity between the case's topics and the juror's posts
case_topics_vector = vectorizer.transform([case_topics])
cosine_similarities = cosine_similarity(case_topics_vector, tfidf_matrix)

data['similarity_score'] = cosine_similarities[0]

2.4 Selecting the Ideal Juror

Finally, you can filter and sort the data based on the criteria most relevant to your case, such as political_affiliation, industry, and similarity_score. This will help you identify the jurors who are most likely to align with your case’s objectives.

# Filter jurors based on political_affiliation and industry
filtered_jurors = data[(data['political_affiliation'] == 'Liberal') & (data['industry'] == 'Healthcare')]

# Sort jurors by similarity_score (in descending order)
sorted_jurors = filtered_jurors.sort_values(by='similarity_score', ascending=False)

# Select the top 10 jurors
selected_jurors = sorted_jurors.head(10)

print(selected_jurors)

Conclusion:

By leveraging Python to analyze firmographic and social media data, you can gain valuable insights into potential jurors and make more informed decisions during juror selection. By understanding a juror’s background, interests, and opinions, you can better anticipate their reactions and attitudes towards your case, ultimately improving your chances of a favorable outcome.