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Khaby Lame

Khabane Lame is a Senegalese social media personality who belongs to Italy. He is very well-known for his TikTok videos, in which he silently mocks overly complicated life hack videos. Lame is the most-followed TikToker and meme creator.

Total Posts
Average Likes
8.37 %
Avg. Eng. Percentage
Avg. Engagement
Average Comments

Content Themes

This shows the account's different Content themes. You can sort by the various parameters and also see the detailed view.

Theme Label Eng % Average Eng. Share of Voice Total Posts Representative Images

Hashtag Sets

This shows the AI-identified most used hashtag sets of the selected account.

#acesocialemdia #acesocialemdia #acesocialemdia #acesocialemdia

Top 5 Hashtags

5 most used hashtags of this account

Rank Hashtag Usage (%)
1 #acesocialemdia 46%
2 #predisit 44%

Hashtags Details

This shows all hashtags with their posts

Hashtag Post Count Eng % Median Likes Median Comments Recent Posts
Support,Service,Creative 120 11 13 13
1 90 11 23 13

Frequently Asked Questions

Khaby Lame has 78689688 followers on instagram as on Jul 26, 2022

Khaby Lame has 8.37 % engagement on instagram between Jan 26, 2022 and Jul 26, 2022

Khaby Lame has total 53 posts on instagram between Jan 26, 2022 and Jul 26, 2022

Khaby Lame has 6536213 average likes on instagram between Jan 26, 2022 and Jul 26, 2022

Khaby Lame has 6583958 as the average engagement on instagram between Jan 26, 2022 and Jul 26, 2022

Khaby Lame has 47744 average comments per post on instagram between Jan 26, 2022 and Jul 26, 2022

Predis.ai's Content Analysis gives you valuable insights into the Instagram account's content. It gives you the content distribution pattern in a sunburst chart. Our AI categorizes the posts and organizes them into levels. In the chart, you can see the categories in the first level (shown in green), second level (shown in blue), and third level (shown in red). If you click on the category, you can see the posts under that category. It gives you an idea about what kind of content the account is posting, which content is dominant, and so on. The post type pattern distribution will tell you the content categories used in different post types. Content distribution pattern for engagement will show you the engagement across different post types.

Our AI categorizes the content into themes and also provides meaningful attributes for those content themes. The AI assigns labels to every theme. You can see the posts categorized under every content theme, the engagement percentage, share of voice, and the number of posts.

The AI analyzes the account and shows the most used hashtags. It clubs the hashtags used together into hashtag sets and also shows you the corresponding posts. You can see the five most used hashtags on that account and their percentage too.

The AI analyzes the account and determines post-performance based on various parameters. You can see the Post Distribution which will tell you the distribution among the type of posts i.e. how many posts of every type (carousel, single image, video). You can also see the Post Distribution across the Weekly Posting Pattern. You can get an idea of when posts were published during the day. You can see the engagement the post types have received in the Engagement Contribution tab. You can see at what times the account is getting engagement across the posts. The AI will tell you how many posts were made a day, and of what type? (posts, carousel, video). In the Engagement Activity, you can see the engagement received across the type of posts every day. You can check the weekly post distribution with respect to each post type. In Engagement Contribution (By post type) you can see when the account gets its engagement in a week. You can also see the top five posts of the account, as well as all posts made from the account.

Sunburst charts, also known as ring charts and radial treemaps, are used to visualize a hierarchical dataset. A Sunburst chart employs a radial layout to provide an immersive visualization of the categorized dataset. A sunburst chart divides the rings based on their hierarchical relationship to the parent node. Each ring can have multiple segments, each of which represents the contribution of a specific dimension in that hierarchy.