sharky
that guy from @betasquad5 & @sds_pod
291,149
Followers
904
Following
75,731
Tweets
990.5
Avg Engagement
139
Listed
No
Verified
Account Overview
What This Report Covers
This comprehensive analysis examines @ohnosharky's Twitter/X presence across multiple dimensions. We analyze tweet content, engagement patterns, audience demographics, posting habits, and network connections to provide actionable insights about this account's social media strategy and influence.
Account Classification
- Account Size: Macro Influencer (100K+)
- Account Age:2010-11-14
- Verification:No
- Location:Not specified
Data Summary
- Tweets Analyzed:3,230
- Avg Likes per Tweet:916.8
- Avg Retweets per Tweet:73.7
- Followers Analyzed:50,000
Engagement Analysis
Based on 3,230 tweetsHis tweets receive high engagement, with an average of 38,372 likes and 3,269 retweets per post.
916.8
Avg Likes/Tweet
73.7
Avg Retweets/Tweet
2,961,169
Total Likes
238,201
Total Retweets
Engagement Quality Analysis
Median-based metrics that resist fake virality and outliers.
230
Median Likes
4
Median Retweets
804
75th Percentile
74,651
Top Tweet
3.4
Engagement per 1K Followers
Normalized influence metric
Viral Spikes
Engagement Pattern
Few posts get most engagement
Posting Behavior
Content style and format preferences
Mixed
Balanced content approach
42.7%
Original
43.7%
Replies
7.7%
Retweets
5.9%
Quotes
0.0%
Threads
0.1%
With Media
28.0%
With Links
3.8%
With #Tags
56.0%
With @Mentions
24.5%
With Emojis
58
Avg Length
Audience Reaction Profile
Conversational
Engages heavily in discussions
1.02
Reply/Original Ratio
0.77
Quote/RT Ratio
Posting Rhythm
Highly Bursty
Posts in concentrated bursts
58.4%
Weekday Posts
41.6%
Weekend Posts
Top Hashtags
Most Mentioned
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Content Strategy Analysis
Based on 3,230 tweetsContent Type Distribution
Content Breakdown
What This Reveals About Their Strategy
This account prioritizes community engagement over content broadcasting. With replies making up the majority of their activity, they invest significant time in conversations, building relationships with followers and participating in discussions. This approach typically leads to higher loyalty and more authentic connections.
The 249 retweets (8% of activity) show they also amplify content from others, which helps build relationships with other accounts and provides value to followers by surfacing relevant content.
Posting Patterns & Optimal Timing
Based on 3,230 tweetsEngagement by Hour (UTC)
Posting Frequency Over Time
Timing Insights
Understanding when an account posts and when their audience is most responsive provides valuable competitive intelligence. The charts above show when this account's content receives the most engagement, which often correlates with when their specific audience is most active on the platform.
Peak posting times vary significantly based on an account's audience demographics, timezone distribution, and content type. Accounts with global audiences often see engagement spread across multiple time windows, while those with regional focus may have more concentrated peaks.
Audience Demographics
Based on 50,000 followersHis audience likely includes football fans and social media users interested in commentary and humor.
Average
Audience Quality
33.3%
Suspicion Index
46.6%
Low-Quality %
5
Median Reach
Audience Quality Signals
Lower percentages indicate healthier, more authentic followers.
57.0%
Empty Bio
48.9%
<10 Tweets
2.3%
Mass Following (>2K)
0.0%
New (<90 days)
71.3%
Low Ratio (<0.1)
0.0%
New (<180 days)
93.9%
No URL
36.2%
<3 Tweets
Free Mode Analysis: These quality metrics use heuristic signals (empty bios, low tweets, suspicious ratios). For ML-powered bot detection with 95%+ accuracy, use our dedicated tool.
Try Bot DetectorFollower Reach & Influence
This account's followers have their own audiences, creating potential for secondary amplification.
19,879,898
Total Potential Reach
5
Median Follower Reach
28
75th Percentile Reach
1.9%
>1K followers
0.2%
>10K followers
0.1%
>50K followers
0.0%
>100K followers
Creator vs Consumer Split
Classification based on tweet activity: Creators (>100 tweets), Consumers (10-100 tweets), Dormant (<10 tweets).
29.8%
Creators
14,919 accounts
21.2%
Consumers
10,616 accounts
48.9%
Dormant
24,465 accounts
Follower Influence Distribution
How many followers do their followers have?
Follower Account Age
How long have followers been on Twitter?
Geographic Distribution
Top locations where followers are based (from those who share location data):
201
Verified Followers
0.2% of total
11,736
Protected Accounts
11.62% of total
3 years
Avg Account Age
of their followers
Notable Followers (By Influence)
Top accounts following this user, sorted by their follower count:
| Username | Followers | Profession | Interests |
|---|---|---|---|
| @yunamusic | 2,436,436 | Musician | Music, Fashion, Beauty |
| @Castro1021 | 1,508,590 | Gamer | Gaming, Champion, Business |
| @KaiCenat | 1,312,637 | Content Creator | Content Creation, KAI, Literature |
| @Squawka | 1,234,197 | Football Analysis | football, stats, analysis |
| @StableRonaldo | 1,226,404 | Gamer | Fortnite, Streaming, Gaming |
| @kio_cyr | 1,147,018 | Influencer | Fashion, Travel, Photography |
| @LeBronJames | 1,005,041 | - | - |
| @EmiratesFACup | 1,003,098 | Sports Organization | Football, Competition, Trophy |
| @fousey | 980,230 | YouTuber | YouTube, Movies, Storytelling |
| @ZachBoychuk | 829,149 | Hockey Player | Hockey, Meme/NFT Trading, Ethereum |
Top Keywords in Follower Bios
Top Hashtags in Follower Bios
45,567
Followers With Bio
55,412
Followers Without Bio
45.13%
% With Bio
54.87%
% Without Bio
Why Audience Demographics Matter
Understanding who follows an account reveals the type of influence they hold. Accounts followed primarily by users with few followers indicate broad, mainstream appeal. Those with many high-follower followers have greater potential for content amplification through secondary sharing.
Account age distribution also tells a story: a follower base with many new accounts might indicate recent viral growth, while established followers suggest long-term, stable influence. Geographic data helps understand the cultural context and timezone spread of the audience.
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Buy Tweets - from $15Viral Tweets
Top 5 by likes+RTs from 3,230 analyzed303,240
Combined Engagement
225,397
Total Likes
77,843
Total Retweets
60,648
Avg per Tweet
Key Metrics for Viral Tweets
0%
With Media
0%
With Hashtags
20%
With @Mentions
20%
With Links
Topical Analysis:
You celebrated every time Sterling scored, you cheered Saka and called him a prodigy when he was the best player on the pitch, you begged for Sancho to play. But now you wanna racially abuse the players and attack black fans? Piss off man
that’s not chunkz that’s shrunkz https://t.co/upvI1w6Pww
the racist england fans after the euros final must be fuming lmaoo
RT @biel_m04: NOTHING PERSONAL .. LONDON IS RED ❤️
3pm games should be televised 3pm games should be televised 3pm games should be televised 3pm games should be televised 3pm games should be televised 3pm games should be televised 3pm games should be televised 3pm games should be televised 3pm games should be televised
Key Insights & Takeaways
He often uses humor and sarcasm to comment on football events and fan behavior. He references specific players and events, indicating a deep knowledge of the sport. His content is likely aimed at engaging with a younger, internet-savvy audience.
Strengths
- + Strong engagement rates above platform average
- + Established follower base of 291,149
- + High community engagement through conversations
- + Active presence with consistent posting
Opportunities
- ~ Optimize posting times for peak engagement windows
- ~ Analyze top-performing content for replicable patterns
- ~ Leverage geographic concentration for targeted content
- ~ Explore collaboration potential with followed accounts
Summary
This analysis of @ohnosharky reveals an well-established Twitter presence with 291,149 followers. The account demonstrates a conversation-first approach, averaging 990.5 engagements per tweet. Key strengths include consistent posting and an engaged audience, with opportunities to further optimize timing and content strategy based on the patterns identified in this report.
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