Thomas Mlambo
Tv Sport Anchor ... @SupersportTV https://supersport.com/ For bookings📧[email protected]
831,013
Followers
929
Following
26,695
Tweets
136.5
Avg Engagement
900
Listed
No
Verified
Account Overview
What This Report Covers
This comprehensive analysis examines @thomasmlambo'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:2009-10-15
- Verification:No
- Location:Johannesburg
Data Summary
- Tweets Analyzed:3,250
- Avg Likes per Tweet:125.1
- Avg Retweets per Tweet:11.4
- Followers Analyzed:50,000
Engagement Analysis
Based on 3,250 tweetsHis tweets typically receive an average of 4,805 likes and 745 retweets, indicating a highly engaged and loyal fan base.
125.1
Avg Likes/Tweet
11.4
Avg Retweets/Tweet
406,714
Total Likes
37,028
Total Retweets
Engagement Quality Analysis
Median-based metrics that resist fake virality and outliers.
35
Median Likes
1
Median Retweets
100
75th Percentile
8,336
Top Tweet
0.16
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
67.9%
Original
27.2%
Replies
1.0%
Retweets
3.9%
Quotes
0.0%
Threads
0.0%
With Media
32.9%
With Links
20.9%
With #Tags
35.5%
With @Mentions
13.1%
With Emojis
89
Avg Length
Audience Reaction Profile
Interactive
Balances broadcasting with conversation
0.4
Reply/Original Ratio
3.71
Quote/RT Ratio
Posting Rhythm
Somewhat Bursty
Occasional posting spikes
64.6%
Weekday Posts
35.4%
Weekend Posts
Top Hashtags
Most Mentioned
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Content Strategy Analysis
Based on 3,250 tweetsContent Type Distribution
Content Breakdown
What This Reveals About Their Strategy
This is a content-creator focused account that primarily shares original thoughts, ideas, and media. With original content dominating their feed, they position themselves as a source of new information rather than a curator. This strategy works well for establishing thought leadership.
The 34 retweets (1% 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 3,250 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 primarily consists of sports fans who enjoy his candid and emotional take on matches and players. They engage with his content through likes and retweets.
Average
Audience Quality
32.7%
Suspicion Index
46.3%
Low-Quality %
22
Median Reach
Audience Quality Signals
Lower percentages indicate healthier, more authentic followers.
57.3%
Empty Bio
51.1%
<10 Tweets
9.3%
Mass Following (>2K)
0.0%
New (<90 days)
55.8%
Low Ratio (<0.1)
0.0%
New (<180 days)
95.7%
No URL
35.3%
<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.
33,280,711
Total Potential Reach
22
Median Follower Reach
100
75th Percentile Reach
3.5%
>1K followers
0.3%
>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).
23.3%
Creators
11,633 accounts
25.7%
Consumers
12,833 accounts
51.1%
Dormant
25,534 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):
38
Verified Followers
0.04% of total
5,183
Protected Accounts
5.13% of total
2 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 |
|---|---|---|---|
| @JohnCena | 14,106,871 | Wrestler | Wrestling, Motivation, Self-promotion |
| @verified | 4,201,696 | - | - |
| @ChrisExcel102 | 1,624,130 | Influencer | Savage, AssHole, King |
| @SinghLions | 1,336,468 | Restaurateur | Food, Travel, Influencer |
| @NkanyeziKubheka | 1,046,931 | Musician | Music, Happiness, Out Now |
| @tumisole | 812,690 | Influencer | Running, Corporate, Influential |
| @HildaNewton21 | 768,418 | Journalist | Habari, Bavicha, Rais |
| @willieeng | 574,088 | Accountant | - |
| @Naked_Dj | 505,558 | DJ | Music, DJing, Producing |
| @THE_THEO_FORD | 470,888 | - | - |
Top Keywords in Follower Bios
Top Hashtags in Follower Bios
50,495
Followers With Bio
50,503
Followers Without Bio
50.0%
% With Bio
50.0%
% 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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Export tweets with engagement metrics, timestamps, media, and reply/retweet details as CSV.
Buy Tweets - from $15Viral Tweets
Top 5 by likes+RTs from 3,250 analyzed33,102
Combined Engagement
28,618
Total Likes
4,484
Total Retweets
6,620
Avg per Tweet
Key Metrics for Viral Tweets
0%
With Media
0%
With Hashtags
20%
With @Mentions
100%
With Links
Topical Analysis:
This was nasty from Saleng 😭😭😭 https://t.co/jIQcCY2id0
Mbappe 😭😭😂😂 https://t.co/KJMZD3BhZG
Mina I know my choice if I were the Chelsea owner https://t.co/bMcxTFXiIG
Parker signs for Galaxy ✍️ https://t.co/DL0gMQCeyR
Make it happen @ChelseaFC https://t.co/RD0g1TwMTM
Key Insights & Takeaways
He often reacts emotionally to sports events, using emojis to express his feelings. He shares content that highlights key moments or standout performances. His tweets are usually short and focused on specific highlights or reactions.
Strengths
- + Strong engagement rates above platform average
- + Established follower base of 831,013
- + Strong original content creation
- + 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 @thomasmlambo reveals an well-established Twitter presence with 831,013 followers. The account demonstrates a content-creator strategy, averaging 136.5 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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