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This is going to make content review so much easier. Thanks for the great content <3

Summary: Narrative Analysis of Peruvian_Bull's Twitter Engagement Introduction Peruvian_Bull, a prominent figure on Twitter, has amassed a substantial following through insightful commentary on finance, stock markets, and economic trends. An analysis of 14,317 tweets reveals significant patterns in content composition, audience engagement, and the impact of various factors such as timing, sentiment, and media inclusion on tweet performance. This narrative explores these findings, providing a comprehensive understanding of how Peruvian_Bull engages with his audience and how certain strategies contribute to higher engagement metrics. Tweet Composition and Character Count The average length of Peruvian_Bull's tweets is 126 characters, indicating a preference for concise messaging within Twitter's character limits. After cleaning the tweets by removing URLs and mentions, the average length decreases to 98 characters. This reduction highlights the frequent use of links and mentions to provide additional context or direct followers to more detailed content. Maximum Lengths: Full Tweets: 458 characters. Cleaned Tweets: 280 characters (Twitter's character limit). Notably, some cleaned tweets have a character count of 0, suggesting they initially contained only URLs or mentions. This emphasizes the importance of supplemental content in his tweeting strategy. Impact on Engagement Longer Tweets (150-200 characters): Show a significant spike in engagement. Retweets: Over 120 on average. Favorites: Exceeding 1,000 on average. Shorter Tweets (<50 characters): Maintain consistent engagement but lower than longer tweets. Retweets: Around 20 on average. Insight: Detailed tweets that provide substantial information tend to resonate more with the audience, leading to higher engagement. Sentiment Analysis and Its Effect on Engagement The sentiment of tweets plays a crucial role in audience interaction. Sentiment Distribution (Full Text): Neutral: 6,146 tweets (43%). Positive: 5,421 tweets (38%). Negative: 2,750 tweets (19%). A comparison of sentiments before and after cleaning the tweets revealed that 138 tweets (0.96%) changed sentiment classification. A Chi-Square test confirmed that this change is statistically significant (p-value of 0.0), indicating that even minor content alterations can impact audience perception. Engagement by Sentiment: Sentiment Average Retweets Average Favorites Negative 17.40 142.19 Neutral 17.12 154.07 Positive 14.20 130.11 Insight: Tweets with negative or neutral sentiments receive higher engagement than positive ones. This suggests that the audience is more responsive to critical analyses or discussions of concerns within the financial markets. Influence of Hashtags, Mentions, and Stock Tickers Hashtag Usage Tweets with Hashtags: 604 tweets (4.22%). Impact on Engagement: Higher visibility and connection to trending topics. Top Hashtag Performance: #Blackrock: 971 retweets, 2,802 favorites. #BigShort: Total engagement of 959. #MMTLP: Total engagement of 942. Mentions and Stock Tickers Tweets with Mentions: 8,801 tweets (61.5%). Tweets with Stock Tickers: 1,509 tweets (10.5%). Engagement Impact: Average Retweets Average Favorites With Tickers 27.54 228.82 Without Tickers 14.66 132.20 Top Performing Stock Tickers: $SCHW: Average 370.5 retweets, 2,062.5 favorites. $MMTLP: Significant engagement with 789 total interactions. $GME: High interest with 462.65 total engagements. Insight: Including stock tickers and relevant hashtags significantly boosts engagement, tapping into active discussions and communities interested in specific financial instruments and companies. Media Presence and Its Correlation with Engagement Tweets with Media Total: 4,097 tweets (28.6%). Average Engagement: Retweets: 32.30. Favorites: 243.53. Tweets without Media Total: 10,220 tweets (71.4%). Average Engagement: Retweets: 9.56. Favorites: 102.30. Correlation Coefficients: Media and Retweets: 0.162 (moderate positive correlation). Media and Favorites: 0.184 (moderate positive correlation). Insight: Including media such as images, charts, or videos significantly enhances tweet engagement. Visual content captures attention and encourages users to interact with the tweet. Timing and Its Influence on Engagement Peak Engagement Times Time of Day: 9 AM: Highest average retweets (~60) and favorites (>400). 11 AM: Second highest engagement. Day of Week: Tuesday: Average Retweets: 20. Average Favorites: ~175. Sunday: Average Retweets: 18. Average Favorites: ~150. Engagement Over Time Growth Periods: March 2022: Account started gaining significant traction. October 2022: Notable spike in engagement, sustained growth thereafter. Consistency: Post week 28, engagement levels remain steady, indicating a loyal and engaged follower base. Insight: Posting during peak hours and on days with higher user activity maximizes visibility and engagement. Aligning content release with audience activity patterns is crucial for optimal reach. Platform Usage and Source Impact on Engagement Engagement by Source Mobile Platforms: Source Average Retweets Average Favorites Mobile Web 20.87 175.25 iPhone 15.32 145.92 Android 9.75 91.39 Desktop Platforms: Source Average Retweets Average Favorites Twitter Web 0.08 3.58 Insight: Tweets posted from mobile platforms receive significantly higher engagement. This may be due to the immediacy and spontaneity of mobile tweeting, allowing for timely responses to market events and trends. Content Categories and Audience Preferences Top Categories by Engagement Retail Trading Phenomena: Average Retweets: 47.40. Average Favorites: 406.51. Topics: Discussions on meme stocks, retail investor behaviors, and platforms influencing the market. Financial Instruments and Derivatives: Average Retweets: 29.69. Average Favorites: 228.61. Topics: Insights into complex financial instruments and their market impact. Banking and Finance: Average Retweets: 24.25. Average Favorites: 172.69. Topics: Analysis of banking trends, financial policies, and economic indicators. Uncategorized Tweets Total: 7,175 tweets. Average Engagement: Retweets: 13.31. Favorites: 135.63. Insight: High engagement in specific categories indicates strong audience interest in particular topics. The large number of uncategorized tweets suggests an opportunity to refine content classification for deeper insights. Audience Preferences and Behavior Interest in Finance-Related Topics: High engagement in tweets discussing retail trading and financial instruments indicates the audience's keen interest in these areas. Preference for Critical Analysis: Higher engagement with neutral and negative sentiments suggests that the audience values critical perspectives and in-depth analysis of market concerns. Engagement with Longer, Detailed Content: Tweets that provide substantial information tend to receive more interaction, highlighting the audience's appetite for detailed insights. Conclusion and Recommendations The analysis of Peruvian_Bull's tweets reveals several key factors that contribute to higher engagement: Content Strategy: Focus on High-Interest Topics: Emphasize content related to retail trading phenomena and financial instruments. Include Stock Tickers and Hashtags: Utilize relevant tickers and trending hashtags to increase visibility. Tweet Composition: Optimal Length: Aim for tweets around 150-200 characters to provide depth while maintaining conciseness. Media Inclusion: Incorporate images, charts, and videos to enhance engagement. Timing: Schedule Posts: Align tweet times with peak audience activity (9 AM and 11 AM) and high-engagement days (Tuesdays and Sundays). Platform Usage: Leverage Mobile Platforms: Continue utilizing mobile platforms for timely and higher-engagement posts. Sentiment Balance: Critical Insights: Provide thoughtful analysis that addresses market concerns, aligning with audience preferences for neutral or negative sentiment content. Audience Interaction: Engage with Followers: Respond to comments and foster discussions to build a stronger community. Monitor Trends: Stay attuned to market developments and audience interests to keep content relevant. By implementing these strategies, Peruvian_Bull can enhance his Twitter presence, foster greater audience engagement, and continue to grow his influence within the financial community. Final Thoughts This comprehensive analysis underscores the importance of understanding audience behavior and preferences. By tailoring content to align with what resonates most with followers, leveraging optimal posting times, and utilizing effective engagement strategies, social media influencers like Peruvian_Bull can maximize their impact and continue to provide valuable insights to their audience.

At 18:08, is output path still good on fabric 2.0 ? I cant seem to make it work !

Thanks for the video. How much does it cost to create each of these GPTs?

I resonate with your process of unifying spiritual studies through Ai and obsidian.

Looks interesting. Would love to see a customizable user interface so it can be white labeled.

Hey mate, that's a really nice project you did a fantastic job . is there any update ? did you finish it ?

I have used the Powershell instead of the cmd terminal and run 'Get-Clipboard | fabric --pattern summarize' instead of using 'pbpaste | fabric --pattern summarize'. It appears to be working since Powershell can be used as cmd and have more capabilities

why not just use the tags feature and the panel

Amazing

Great!! But how can I integrate my API key from OpenAI in Obsidian?

I created a fabric plugin for Obsidian, find it in the community plugin list under “unofficial fabric integration”

What is fabric?

I see a penguin ! Tux The Linux Mascot. Great video and thanks for the tips.

You're off your head in best way possible. Love your videos and ideas sir.

I am a bit lost. My environment variable do not have the `FABRIC_OUTPUT_PATH`. Can you walk through as to how you saved it in the first place? (As fabric setup doesn't call for it)

could not find the fabric config file .. is this the same fabric as from fabric.so or its something different .. btw i am using mac so i believe things wont change much as you share the unix commands.

This is the video I’ve been wanting! Glad to find you

I watched this Video when it when you released it, I was intrigued by FABRIC & OBSIDIAN , spent some time familiarising myself with them, came back here & it all "makes sense" ::: 😉

使用一些chrome浏览器插件？ RUclips To Text

How do you set up and download Fabric? Like the Fabric I found is not looking like that command prompt

Great information Thanks a lot. aloha

Too bad Alex Hormozi is a moron

Yes! The Gateway Experience is awesome. I finished the meditation series, and I've been making progress the whole day long.

Nice video, its a bit rough, maybe would be worthwile to explain what fabric is and what are the different syntaxes being used for extraction. Im interested in this topic as well and you are one of first who have videos about it. Definitely learnt something new :) Keep it up.

Curly brackets :}

It's called a curly brace bro😄

How do you configure it so that it goes directly to your directory? when you add |save ##note_name

This was a very informative video thanks but can we get a tutorial on how you made your video in video box

There is a Fabric plugin for this in Obsidian community plugins

great workflow! How did you setup fabric to save to obsidian?

I'd look forward to your real gateway experience video. I finished it. It is wonderful.

My good man watch Network Chuck too.

Nice Video - Thanks for sharing. What was more interesting is your Obsidian Setup. I will be happy if you can share a video explaining your Obsidian Setup and your Knowledge Workflow. Specially I want to see how you use the Tags folder. Is it similar to MOCs.