andrea masi
Ex professional rugby player •Coach @RCTofficiel
87,475
Followers
1,165
Following
2,095
Tweets
35.5
Avg Engagement
154
Listed
No
Verified
Account Overview
What This Report Covers
This comprehensive analysis examines @andrea_masi'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: Mid-Tier (10K+)
- Account Age:2011-01-30
- Verification:No
- Location:Toulon
Data Summary
- Tweets Analyzed:2,067
- Avg Likes per Tweet:7.1
- Avg Retweets per Tweet:28.5
- Followers Analyzed:10,999
Engagement Analysis
Based on 2,067 tweetsHis tweets typically receive high engagement, with an average of 781 likes and 182 retweets. The content is interactive, often prompting discussions and reactions from his followers.
7.1
Avg Likes/Tweet
28.5
Avg Retweets/Tweet
14,653
Total Likes
58,816
Total Retweets
Engagement Quality Analysis
Median-based metrics that resist fake virality and outliers.
0
Median Likes
1
Median Retweets
8
75th Percentile
20,047
Top Tweet
0.41
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
25.2%
Original
44.1%
Replies
28.4%
Retweets
2.3%
Quotes
0.0%
Threads
0.6%
With Media
38.9%
With Links
22.7%
With #Tags
86.0%
With @Mentions
18.2%
With Emojis
83
Avg Length
Audience Reaction Profile
Conversational
Engages heavily in discussions
1.75
Reply/Original Ratio
0.08
Quote/RT Ratio
Posting Rhythm
Highly Bursty
Posts in concentrated bursts
70.8%
Weekday Posts
29.2%
Weekend Posts
Top Hashtags
Most Mentioned
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Content Strategy Analysis
Based on 2,067 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 587 retweets (28% 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 2,067 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 10,999 followersHis followers include rugby fans, sports enthusiasts, and individuals interested in personal development and humor. The audience is primarily male, with a strong interest in sports and lifestyle content.
Below Average
Audience Quality
42.5%
Suspicion Index
65.3%
Low-Quality %
3
Median Reach
Audience Quality Signals
Lower percentages indicate healthier, more authentic followers.
71.5%
Empty Bio
71.1%
<10 Tweets
5.9%
Mass Following (>2K)
0.0%
New (<90 days)
76.4%
Low Ratio (<0.1)
0.0%
New (<180 days)
94.3%
No URL
59.1%
<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.
7,452,650
Total Potential Reach
3
Median Follower Reach
14
75th Percentile Reach
3.0%
>1K followers
0.6%
>10K followers
0.2%
>50K followers
0.1%
>100K followers
Creator vs Consumer Split
Classification based on tweet activity: Creators (>100 tweets), Consumers (10-100 tweets), Dormant (<10 tweets).
17.5%
Creators
1,922 accounts
11.5%
Consumers
1,260 accounts
71.1%
Dormant
7,817 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):
15
Verified Followers
0.14% of total
441
Protected Accounts
4.01% of total
7 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 |
|---|---|---|---|
| @soledadobrien | 1,317,054 | Journalist | working, mom, journalist |
| @triomedusa | 991,478 | - | - |
| @the_worldface | 551,874 | Media | Media, News, Publishing |
| @JamesMelville | 437,511 | Consultant | Communications, Sponsorship, Writing |
| @HarryGoaz | 234,402 | - | - |
| @rugbyworldcupjp | 217,640 | Sports Marketing | rugby, world cup, japan |
| @MaxGazze | 201,758 | Musician | Music, Italy, Art |
| @willcarling | 178,127 | Rugby Player | Rugby, Cycling, Family |
| @Arabelladaho | 121,645 | - | - |
| @davidflatman | 116,342 | Rugby Player | rugby, humor, dance |
Top Keywords in Follower Bios
Top Hashtags in Follower Bios
3,134
Followers With Bio
7,865
Followers Without Bio
28.49%
% With Bio
71.51%
% 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 2,067 analyzed35,242
Combined Engagement
2,311
Total Likes
32,931
Total Retweets
7,048
Avg per Tweet
Key Metrics for Viral Tweets
0%
With Media
0%
With Hashtags
80%
With @Mentions
80%
With Links
Topical Analysis:
RT @itsMichaelJ: This is for all the Athletes... http://t.co/OSQQXq5m
RT @rioferdy5: After going to the Eng vs Wales rugby game..watching a footballer roll around after a little tap on the face as if poll axed is embarrassing
RT @Redordead8: Can every1 retweet this so that this woman never has the chance to attend a game again @LaLiga http://t.co/zgAegvvk3E
I’ve got to pass this British History test tomorrow as part of my application to remain in UK. Wondering how many British would pass this test… 🤔 https://t.co/eMSxadCd6y
RT @iomatrix23: 😄😄😄😄😄💪💪💪💪💪 http://t.co/kuc8jkRC
Key Insights & Takeaways
He uses personal challenges, like failing a British History test, to connect with his audience and highlight his human side. He frequently shares rugby-related content, showcasing his expertise and passion for the sport. He engages with his followers through humor and relatable topics, which helps maintain a strong and active community.
Strengths
- + Established follower base of 87,475
- + 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 @andrea_masi reveals an growing Twitter presence with 87,475 followers. The account demonstrates a conversation-first approach, averaging 35.5 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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