This dataset contains 30,000 Instagram posts with detailed analytics generated to mimic real Instagram Insights behavior from the past 12 months. It includes media performance indicators such as likes, comments, shares, saves, reach, impressions, and engagement rate. The data reflects realistic patterns of Instagram’s algorithm and content performance trends, including Reels, Photos, Videos, and Carousels.

This dataset is ideal for: Social media analytics Trend prediction Engagement modeling Machine learning Influencer performance analysis Dashboard creation Growth forecasting Reels vs. Posts comparisons

All upload dates are within the previous 365 days, making the dataset time-relevant and aligned with current Instagram usage behavior.

COLUMN DESCRIPTIONS

Post_ID A unique ID for each Instagram post. Useful as a primary key.

Upload_Date The date the post was uploaded, within the last 1 year.

Media_Type Type of content: Photo, Video, Reel, or Carousel. Important for performance comparisons.

Likes Number of likes received by the post.

Comments Total comments the post received.

Shares How many times users shared the post.

Saves Number of users who saved the content. A major Instagram ranking factor.

Reach Unique accounts who saw the post.

Impressions Total views from all sources (may exceed reach).

Caption_Length Number of characters in the post caption.

Hashtags_Count Total hashtags used.

Followers_Gained How many followers were gained directly from this post.

Traffic_Source Where viewers discovered the post: Explore, Home Feed, Hashtags, Profile, Reels Feed, or External.

Engagement_Rate Percentage of engagement relative to impressions. Useful for performance scoring.

WHY THIS DATASET IS USEFUL

Instagram’s algorithm rewards: High saves Strong engagement rates Reels performance Discoverability through Explore Good content retention This dataset allows analysis of: High-performing media types Which traffic sources bring the most reach Ideal hashtag usage Relationship between caption length and engagement Factors influencing followers gained Predicting engagement rate trends

Discover high-performing influencers with our comprehensive Instagram Influencers dataset. Access critical metrics including follower counts, engagement rates, verified status, business categories, and bio information. Analyze top posts, profile details, related accounts, and contact information to identify the perfect influencers for your brand partnerships and marketing campaigns. Millions of influencer records available Price starts at $250/100K records Data formats are available in JSON, NDJSON, CSV, XLSX and Parquet. 100% ethical and compliant data collection Included datapoints:

Account Fbid Id Followers Posts Count Is Business Account Is Professional Account Is Verified Avg Engagement External Url Biography Business Category Name Category Name Following Posts (Top Posts Data) Profile Image Link Profile URL Profile Name Highlights Count Full Name Is Private Bio Hashtags URL Is Joined Recently Has Channel Partner ID Business Address Related Accounts Email Address And much more

This dataset tracks influencer marketing campaigns across major social media platforms, providing a robust foundation for analyzing campaign effectiveness, engagement, reach, and sales outcomes. Each record represents a unique campaign and includes details such as the campaign’s platform (Instagram, YouTube, TikTok, Twitter), influencer category (e.g., Fashion, Tech, Fitness), campaign type (Product Launch, Brand Awareness, Giveaway, etc.), start and end dates, total user engagements, estimated reach, product sales, and campaign duration. The dataset structure supports diverse analyses, including ROI calculation, campaign benchmarking, and influencer performance comparison.

Columns: - campaign_id: Unique identifier for each campaign
- platform: Social media platform where the campaign ran
- influencer_category: Niche or industry focus of the influencer
- campaign_type: Objective or style of the campaign
- start_date, end_date: Campaign time frame
- engagements: Total user interactions (likes, comments, shares, etc.)
- estimated_reach: Estimated number of unique users exposed to the campaign
- product_sales: Number of products sold as a result of the campaign
- campaign_duration_days: Duration of the campaign in days

Getting Started with the Data

1. Load and Inspect the Dataset

import pandas as pd

df = pd.read_csv('influencer_marketing_roi_dataset.csv', parse_dates=['start_date', 'end_date'])
print(df.head())
print(df.info())

2. Basic Exploration

# Overview of campaign types and platforms
print(df['campaign_type'].value_counts())
print(df['platform'].value_counts())

# Summary statistics
print(df[['engagements', 'estimated_reach', 'product_sales']].describe())

3. Engagement and Sales Analysis

# Average engagements and sales by platform
platform_stats = df.groupby('platform')[['engagements', 'product_sales']].mean()
print(platform_stats)

# Top influencer categories by product sales
top_categories = df.groupby('influencer_category')['product_sales'].sum().sort_values(ascending=False)
print(top_categories)

4. ROI Calculation Example

# Assume a fixed campaign cost for demonstration
df['campaign_cost'] = 500 + df['estimated_reach'] * 0.01 # Example formula

# Calculate ROI: (Revenue - Cost) / Cost
# Assume each product sold yields $40 revenue
df['revenue'] = df['product_sales'] * 40
df['roi'] = (df['revenue'] - df['campaign_cost']) / df['campaign_cost']

# View campaigns with highest ROI
top_roi = df.sort_values('roi', ascending=False).head(10)
print(top_roi[['campaign_id', 'platform', 'roi']])

5. Visualizing Campaign Performance

import matplotlib.pyplot as plt
import seaborn as sns

# Engagements vs. Product Sales scatter plot
plt.figure(figsize=(8,6))
sns.scatterplot(data=df, x='engagements', y='product_sales', hue='platform', alpha=0.6)
plt.title('Engagements vs. Product Sales by Platform')
plt.xlabel('Engagements')
plt.ylabel('Product Sales')
plt.legend()
plt.show()

# Average ROI by Influencer Category
category_roi = df.groupby('influencer_category')['roi'].mean().sort_values()
category_roi.plot(kind='barh', color='teal')
plt.title('Average ROI by Influencer Category')
plt.xlabel('Average ROI')
plt.show()

6. Time-Based Analysis

# Campaigns over time
df['month'] = df['start_date'].dt.to_period('M')
monthly_sales = df.groupby('month')['product_sales'].sum()
monthly_sales.plot(figsize=(10,4), marker='o', title='Monthly Product Sales from Influencer Campaigns')
plt.ylabel('Product Sales')
plt.show()

Use Cases

• ROI Analysis: Quantify the return on investment for influencer campaigns across platforms and categories.
• Campaign Benchmarking: Compare campaign performance by type, influencer niche, or platform.
• Trend Analysis: Track engagement, reach, and sales trends over time.
• Influencer Selection: Identify high-performing influencer categories and campaign types for future partnerships.

Description

The top Instagram influencers and celebrities in the globe are included in this dataset. It contains important parameters like nation, average likes, total posts, number of followers, engagement rate, and worldwide ranking. The dataset aids in the analysis of Instagram audience engagement, influencer performance, and online popularity.

Data science initiatives, digital marketing tactics, influencer marketing research, and social media analysis may all benefit from it. This dataset may be used by researchers, students, and marketers to examine trends in online celebrity, contrast influencers and celebrities, and comprehend the relationship between follower numbers and engagement.

Instagram is an American photo and video sharing social networking service founded in 2010 by Kevin Systrom and Mike Krieger, and later acquired by Facebook Inc.. The app allows users to upload media that can be edited with filters and organized by hashtags and geographical tagging. Posts can be shared publicly or with preapproved followers. Users can browse other users' content by tag and location, view trending content, like photos, and follow other users to add their content to a personal feed.

Instagram network is very much used to influence people (the users followers) in a particular way for a specific issue - which can impact the order in some ways.

This dataset supports research on how Instagram Influencers impact female consumer behaviour to purchase products and the role of factors such as envy, scepticism towards advertising, satisfaction with life, social comparison and maternalism on consumer behaviour. There are two different files. The SPSS and CVS spreadsheet files include the same dataset but in a different format.

This dataset contains 33,935 Instagram influencers who are classified into the following nine categories including beauty, family, fashion, fitness, food, interior, pet, travel, and other. We collect 300 posts per influencer so that there are 33,935x330 = 10,180,500 Instagram posts in the dataset.

The dataset includes two types of files, post metadata and image files.

1) Post metadata files are in JSON format and contain the following information: caption, usertags, hashtags, timestamp, sponsorship, likes, comments, etc. Its size is at about 37GB.

2) Image files are in JPEG format and the dataset contains 12,933,406 image files since a post can have more than one image file. The total size of these image files is 189GB.

If a post has only one image file then the JSON file and the corresponding image files have the same name. However, if a post has more than one image then the JSON file and corresponding image files have different names. Therefore, we also provide a JSON-Image_mapping file that shows a list of image files that corresponds to post metadata.

If you want to use this dataset, please cite it accordingly. The data can be accessed on the respective website link below.

"Multimodal Post Attentive Profiling for Influencer Marketing," Seungbae Kim, Jyun-Yu Jiang, Masaki Nakada, Jinyoung Han and Wei Wang. In Proceedings of The Web Conference (WWW '20), ACM, 2020.

This is a Data analysis Project. I used Excel to clean up the messy raw data about the "Top 100 Instagram Influencers" and turn it into a neat, easy-to-read table. Then, I dove into the data on the Top 100 Instagram influencers, using Excel to uncover interesting insights about which countries were leading in terms of engagement and reach. I made sense of it all by setting up Pivot tables to connect the dots and bring together the most important information. To make sure everyone could understand the findings, I got creative and visualized the data, creating a dynamic dashboard that made it easy to see what was going on at a glance.

Gain valuable insights with our comprehensive Social Media Dataset, designed to help businesses, marketers, and analysts track trends, monitor engagement, and optimize strategies. This dataset provides structured and reliable social media data from multiple platforms.

Dataset Features

User Profiles: Access public social media profiles, including usernames, bios, follower counts, engagement metrics, and more. Ideal for audience analysis, influencer marketing, and competitive research. Posts & Content: Extract posts, captions, hashtags, media (images/videos), timestamps, and engagement metrics such as likes, shares, and comments. Useful for trend analysis, sentiment tracking, and content strategy optimization. Comments & Interactions: Analyze user interactions, including replies, mentions, and discussions. This data helps brands understand audience sentiment and engagement patterns. Hashtag & Trend Tracking: Monitor trending hashtags, topics, and viral content across platforms to stay ahead of industry trends and consumer interests.

Customizable Subsets for Specific Needs Our Social Media Dataset is fully customizable, allowing you to filter data based on platform, region, keywords, engagement levels, or specific user profiles. Whether you need a broad dataset for market research or a focused subset for brand monitoring, we tailor the dataset to your needs.

Popular Use Cases

Brand Monitoring & Reputation Management: Track brand mentions, customer feedback, and sentiment analysis to manage online reputation effectively. Influencer Marketing & Audience Analysis: Identify key influencers, analyze engagement metrics, and optimize influencer partnerships. Competitive Intelligence: Monitor competitor activity, content performance, and audience engagement to refine marketing strategies. Market Research & Consumer Insights: Analyze social media trends, customer preferences, and emerging topics to inform business decisions. AI & Predictive Analytics: Leverage structured social media data for AI-driven trend forecasting, sentiment analysis, and automated content recommendations.

Whether you're tracking brand sentiment, analyzing audience engagement, or monitoring industry trends, our Social Media Dataset provides the structured data you need. Get started today and customize your dataset to fit your business objectives.

Instagram is an American photo and video sharing social networking service founded in 2010 by Kevin Systrom and Mike Krieger, and later acquired by American company Facebook Inc., now known as Meta Platforms. The app allows users to upload media that can be edited with filters and organized by hashtags and geographical tagging. Posts can be shared publicly or with preapproved followers. Users can browse other users' content by tag and location, view trending content, like photos, and follow other users to add their content to a personal feed.

Instagram network is very much used to influence people (the users followers) in a particular way for a specific issue - which can impact the order in some ways.

Data Dictionary

The Top Instagram Accounts Dataset is a collection of 200 rows of data that provides valuable insights into the most popular Instagram accounts across different categories. The dataset contains several columns that provide comprehensive information on each account's performance, engagement rate, and audience size.

1. The "rank": column lists the accounts in order of their popularity on Instagram, starting from the most followed account.

2. The "name": column displays the Instagram handle of the account, which can be used to locate and follow the account on Instagram.

3. The "channel_info": column provides a brief description of the account, such as the type of content it features or the products and services it offers.

4. The "Category": column categorizes the account based on its primary theme or subject matter, such as fashion, sports, entertainment, or food.

5. The "posts": column displays the total number of posts on the account. This column helps to understand the account's level of activity and the amount of content it has produced over time.

6. The "followers": column indicates the number of people who follow the account on Instagram.

7. The "avg likes": column displays the average number of likes that the account's posts receive per post.

8. The "eng rate": column calculates the account's engagement rate by dividing the total number of likes and comments received by the total number of followers, expressed as a percentage.

How you can use this Dataset?

The Top Instagram Accounts Dataset can be used in a variety of ways to gain insights into the performance and engagement levels of popular Instagram accounts. Here are a few examples of what you can do with this dataset:

1. Conduct category analysis: The dataset provides information on the category of each Instagram account. You can use this information to conduct a category analysis and identify the most popular categories on Instagram.

2. Identify top influencers: The dataset ranks Instagram accounts based on their follower count. You can use this information to identify the top influencers in different categories and use them for influencer marketing campaigns.

3. Analyze engagement levels: The dataset includes columns such as "avg likes" and "eng rate" that provide insights into the engagement levels of Instagram accounts. You can use this information to understand what type of content resonates with Instagram users and create more engaging content for your own account.

Context

Discover 1000 Top Ranked Influencers by Type and Category of Influence in United States.

Acknowledgements

Data source: https://starngage.com/app/global/influencer/ranking/united-states

data Source - https://statso.io/instagram-reach-analysis-case-study/

Certainly! Let's conduct a case study on Instagram reach analysis. To make the case study more specific, let's imagine a scenario where a fashion brand called "Fashionista" wants to analyze the reach of their Instagram account over the past six months.

Objective: Analyze the reach of Fashionista's Instagram account and identify trends, patterns, and insights that can help improve their reach and engagement.

Steps for the Instagram Reach Analysis:

1. Data Collection:

   • Gather data from Fashionista's Instagram account for the past six months.
   • Collect metrics such as follower count, post reach, impressions, likes, comments, and engagement rate.
   • Use Instagram's built-in analytics or third-party tools like Iconosquare or Sprout Social to retrieve the necessary data.

2. Define Key Metrics:

   • Identify the key metrics that will help assess the reach of Fashionista's Instagram account.
   • Key metrics may include follower growth rate, average reach per post, total impressions, engagement rate, and engagement per post.

3. Analyze Follower Growth:

   • Plot the follower count over the past six months to observe any trends.
   • Calculate the follower growth rate to understand the rate at which the account is gaining or losing followers.
   • Look for any significant changes in follower count and investigate potential reasons behind those changes.

4. Evaluate Post Reach and Impressions:

   • Analyze the average reach per post and total impressions to understand the reach of Fashionista's content.
   • Identify posts with the highest and lowest reach and compare their characteristics.
   • Look for patterns or themes that resonate well with the audience and those that underperform.

5. Assess Engagement:

   • Calculate the average engagement rate and compare it across different types of content (e.g., images, videos, stories, reels).
   • Identify posts with the highest engagement rate and analyze their content, captions, and hashtags.
   • Look for patterns or elements that encourage higher engagement from the audience.

6. Identify Optimal Posting Times:

   • Analyze the data to identify the days and times when Fashionista's posts receive the highest reach and engagement.
   • Experiment with posting at different times and measure the impact on reach and engagement.

7. Monitor Competitors:

   • Analyze the reach and engagement of Fashionista's competitors' Instagram accounts.
   • Identify strategies or content types that work well for competitors and consider adopting similar approaches if relevant.

8. Generate Insights and Recommendations:

   • Summarize the findings from the analysis and identify key insights and trends.
   • Recommend strategies to improve Fashionista's Instagram reach based on the insights obtained.
   • Provide actionable recommendations such as optimizing content, using relevant hashtags, collaborating with influencers, or running Instagram ads.

By conducting a thorough analysis of Fashionista's Instagram reach, you'll gain valuable insights into their audience's behavior, content performance, and engagement patterns. These insights can help guide future content strategies and optimize reach and engagement on Instagram.

This dataset provides comprehensive social media profile links discovered through real-time web search. It includes profiles from major social networks like Facebook, TikTok, Instagram, Twitter, LinkedIn, Youtube, Pinterest, Github and more. The data is gathered through intelligent search algorithms and pattern matching. Users can leverage this dataset for social media research, influencer discovery, social presence analysis, and social media marketing. The API enables efficient discovery of social profiles across multiple platforms. The dataset is delivered in a JSON format via REST API.

Social Media User Behavior & Lifestyle – 1 Million Synthetic Users (Instagram 2025–2026 based)

This dataset contains 1,000,000+ fully synthetic user profiles that realistically simulate Instagram usage patterns combined with detailed demographic, lifestyle, health, and behavioral attributes.

All data is 100% synthetic — generated using statistical distributions and realistic correlations.
No real user data was used or collected. Perfectly safe for research, education, prototyping, and Kaggle competitions.

Dataset Overview

• Rows: 1,000,000 users
• Columns: 57
• File: instagram_usage_lifestyle_1million.csv (~250–350 MB uncompressed)
• License: CC0: Public Domain (use, modify, share freely)

Key Themes & Columns

Demographics & Background (19 columns)
age, gender, country, urban_rural, income_level, employment_status, education_level, relationship_status, has_children

Lifestyle & Health (10 columns)
exercise_hours_per_week, sleep_hours_per_night, diet_quality, smoking, alcohol_frequency, perceived_stress_score, self_reported_happiness, body_mass_index, blood_pressure_systolic, blood_pressure_diastolic, daily_steps_count

Daily/Weekly Habits (8 columns)
weekly_work_hours, hobbies_count, social_events_per_month, books_read_per_year, volunteer_hours_per_month, travel_frequency_per_year

Instagram Usage & Engagement (17 columns)
daily_active_minutes_instagram, sessions_per_day, posts_created_per_week, reels_watched_per_day, stories_viewed_per_day, likes_given_per_day, comments_written_per_day, dms_sent_per_week, dms_received_per_week, ads_viewed_per_day, ads_clicked_per_day, time_on_feed_per_day, time_on_explore_per_day, time_on_messages_per_day, time_on_reels_per_day, followers_count, following_count, notification_response_rate, average_session_length_minutes, content_type_preference, preferred_content_theme, privacy_setting_level, two_factor_auth_enabled, biometric_login_used, linked_accounts_count, subscription_status, user_engagement_score, account_creation_year, last_login_date, uses_premium_features

Built-in Realistic Correlations

• Higher perceived stress → increased daily active minutes & more reels/stories consumption
• Lower self-reported happiness → longer sessions & higher feed/reels time
• Younger age → significantly higher reels watched, posts created, follower growth
• More exercise & better sleep → slightly higher happiness & lower compulsive usage
• Higher income → increased likelihood of premium features & business accounts

Use Cases

• Exploratory Data Analysis (EDA) – discover links between screen time & well-being
• Predictive modeling – predict happiness/stress/sleep from usage patterns
• Clustering – identify user personas (doom-scroller, casual poster, aspiring influencer, etc.)
• A/B testing simulation – study behavioral effects
• Education & tutorials – great for teaching correlation, regression, feature engineering, large dataset handling
• Benchmarking – compare synthetic patterns vs real-world social media studies
• 100% synthetic – no real user data was accessed or used

Feedback, notebooks, or extensions welcome in the discussion tab.

Instagram[a] is a photo and video sharing social networking service founded in 2010 by Kevin Systrom and Mike Krieger, and later acquired by American company Facebook Inc. The app allows users to upload media that can be edited with filters and organized by hashtags and geographical tagging. Posts can be shared publicly or with preapproved followers. Users can browse other users' content by tag and location, view trending content, like photos, and follow other users to add their content to a personal feed.

Instagram was originally distinguished by allowing content to be framed only in a square (1:1) aspect ratio of 640 pixels to match the display width of the iPhone at the time. In 2015, this restrictions was eased with an increase to 1080 pixels. It also added messaging features, the ability to include multiple images or videos in a single post, and a Stories feature—similar to its main competitor Snapchat—which allowed users to post their content to a sequential feed, with each post accessible to others for 24 hours. As of January 2019, Stories is used by 500 million people daily.

This dataset comprises of the details of top 1000 influencers in instagram

Context

This dataset is for local (Saudi Arabia) social media influencers, and the dataset is built using web scraping to get influencers information from https://influence.co/category/riyadh . The dataset focused on Instagram influencers in Saudi Arabia and contains 5 attributes and 243 rows. In particular, the dataset has the Instagram id for the influencers,number of followers, the category name that they belong to and level of impact of influencers on Instagramwhich is the avg engagement rate.

Content

# Data Set Information:

• IG_id, The influencer Instagram id, object.
• No_followers, The number of followers the influencer have, int64.
• Category_name, the category which the influencer belongs to (# Here I assumed that when there were other social media platforms, I would replace them with the name of persons in these programs, for example, 'snapchat - lifestyle', youtube - vlogger','Facebook, Blogger'), object.
• Locations, the influencer location (based city), object.
• engagment_rate_avg , the engagement rate the influencer have in % ,float64.

Acknowledgements

Data source : https://influence.co/category/riyadh

Fake Instagram Profile Detection Dataset This dataset is designed for training and evaluating machine learning models to detect fake Instagram profiles. It contains various features extracted from Instagram user profiles, such as username characteristics, profile descriptions, follower counts, and engagement metrics. The dataset can be used for classification tasks to identify whether a profile is real or fake.

Dataset Features Profile Picture Availability: Whether the user has a profile picture. Numerical Ratio in Username: The proportion of numbers in the username. Full Name Analysis: Number of words in the full name and its numerical ratio. Username and Name Matching: Whether the username and full name are identical. Description Length: Number of characters in the user’s bio/description. External URL Presence: Whether the profile contains an external link. Account Privacy: Indicates whether the profile is private or public. Post Count: Total number of posts uploaded by the user. Follower Count: Number of followers the user has. Following Count: Number of accounts the user follows. Label Information The dataset is labeled to classify profiles as either fake (1) or real (0) based on the given features. Probability scores are also provided for classification.

Use Cases Machine learning classification models for fake profile detection. Exploratory data analysis on Instagram profile patterns. Feature engineering and data preprocessing for social media fraud detection. This dataset can Only be used by students and researchers working on cybersecurity not for production, social media analysis, and artificial intelligence applications.

About Dataset This dataset captures the pulse of viral social media trends across Facebook, Instagram and Twitter. It provides insights into the most popular hashtags, content types, and user engagement levels, offering a comprehensive view of how trends unfold across platforms. With regional data and influencer-driven content, this dataset is perfect for:

Trend analysis 🔍 Sentiment modeling 💭 Understanding influencer marketing 📈 Dive in to explore what makes content go viral, the behaviors that drive engagement, and how trends evolve on a global scale! 🌍

Analyzing user behavior on Instagram📱🤳🙎‍♂️ can provide valuable insights into user engagement, preferences, and overall user experience. As a social media platform, Instagram offers various opportunities to study user behavior. Here are some aspects to consider when analyzing user behavior on Instagram:

User Engagement Metrics:

Likes: Analyze the number of likes on posts to understand which content resonates well with users. Comments: Study the comments on posts to gauge the level of interaction and engagement. Shares: Examine the number of times users share posts to understand content virality. Content Analysis:

Content Types: Identify the types of content (photos, videos, stories, etc.) that receive the highest engagement. Hashtags: Study the effectiveness of different hashtags in driving user interactions. Captions: Analyze the impact of captions on engagement and user response. User Demographics:

Age, Gender, and Location: Understand the demographics of the user base and how it affects behavior. Active Hours: Determine when users are most active to optimize content posting times. Follower Growth and Churn:

Follower Growth: Track the growth rate of followers over time. Follower Churn: Investigate the reasons for follower losses or unfollows. Influencers and Brand Advocacy:

Identify influential users who drive engagement and contribute to brand advocacy. Measure the impact of influencer collaborations on engagement and brand reach. Story Engagement:

Analyze user interactions with Instagram Stories, such as taps, swipes, and exits. Assess the performance of Stories compared to regular posts. Explore Page and Discoverability:

Study the factors that influence content appearing on users' Explore pages. Analyze the impact of Instagram's algorithms on content discoverability. User Journey Analysis:

Track user interactions from initial discovery to conversion (e.g., website visits or product purchases). Understand the user journey and potential drop-off points. User Sentiment Analysis:

Conduct sentiment analysis on comments and captions to gauge user feelings towards content or products. Ad Engagement:

Analyze user interactions with sponsored posts and advertisements. Measure the effectiveness of ad campaigns in driving engagement and conversions. User Activity Patterns:

Study the frequency and duration of user interactions on the platform. Identify peak activity times and user behavior during different days of the week. Privacy and Data Ethics:

Ensure compliance with user privacy regulations and data protection policies. Respect user data and privacy while conducting the analysis. Remember, Instagram's API access and data usage policies might limit the depth of analysis. As such, ensure compliance with Instagram's terms of service and privacy guidelines while performing user behavior analysis on the platform.