Audience

Culling from followers, likers, commenters, etc., we extract sample audience accounts along with their bios and posts. Our AI/ML algorithms then craft distributions based on this data.

1. Core Audience Statistics

1.1. Top Gender

Our AI/ML algorithms help determine the predominant gender within an audience by analyzing various data points such as usernames, profile pictures, biographical information, and engagement patterns. These algorithms employ natural language processing (NLP) and image recognition to infer gender based on linguistic cues, visual characteristics, and behavioral patterns in social media interactions. By processing large datasets and detecting patterns, our AI/ML algorithms can generate insights regarding the top gender composition of an audience with a high degree of accuracy.

1.2. Top Age Group

Our AI/ML algorithms leverage user-provided birthdates, language patterns, interests, engagement behaviors, and visual characteristics such as profile pictures to estimate the dominant age group within an audience. By analyzing these factors comprehensively, our algorithms provide insights into the primary age demographic of the audience.

1.3. Top City

Our AI/ML algorithms utilize geolocation data, language preferences, content engagement patterns, and information such as cities mentioned/tagged in profile bios or posts to infer the top cities of an audience. By analyzing these factors comprehensively, the algorithms can accurately identify the cities where most of the audience is located.

1.4. Authentic Audience

Our AI/ML algorithms assess engagement patterns, including likes, comments, shares, and clicks, to gauge the level of interaction with the influencer's content. Genuine followers demonstrate consistent and meaningful engagement over time. Our algorithms also examine account activity, such as posting frequency and profile updates, as genuine followers typically maintain regular activity patterns. Additionally, our AI algorithms evaluate profile completeness, considering factors like profile pictures and biographical information, to distinguish genuine followers from fake or inactive accounts. By analyzing user behavior, including content preferences and interaction timing, our algorithms can identify natural engagement patterns exhibited by genuine followers. Network analysis is also utilized to detect suspicious patterns in follower connections. Notably, our AI/ML algorithms exclude mass followers—accounts following more than 1500—as the likelihood of meaningful interaction with each influencer is minimal within such large follower lists.

2. Gender Distribution

The gender distribution graph illustrates the percentage distribution of the audience based on gender.

3. Age Group and Gender Distribution

This metric provides the percentage distribution of the audience within a specific age group, considering gender distinctions.

4. Audience Interests

Our AI/ML algorithms analyze user-generated content, engagement patterns, social interactions, and explicit preferences to identify an audience's interests. By examining content engagement, social connections, and user preferences, these algorithms generate insights into the topics and themes that resonate most with the audience. Our algorithms are continuously learning and adapting to provide high-accuracy results.

5. City Distribution

This metric unveils the percentage distribution of the primary audience across different cities, providing insights into the geographic distribution of the audience.

6. Audience Type

This metric categorizes the audience into different types, distinguishing between real followers, fake followers, influencers, and mass followers.

7. Top Comments

This metric involves the identification and classification of comments into categories such as "good," "neutral," and "bad." The categorization offers insights into the sentiment and nature of the top comments associated with a post or content.

8. Comment Buzz Words

These are specific words or phrases frequently used in the comments section by the audience in response to an influencer's content. Analyzing comment buzz-words helps identify recurring themes, sentiments, or topics that resonate with the audience and can provide valuable insights into the influencer's content impact and audience preferences.