Monitors social media, flags sentiment spikes and drafts alerts Rust

```rust
use std::collections::HashMap;

// A simple struct to represent a social media post.  In a real application,
// this would be much more complex, including things like author, date, etc.
#[derive(Debug, Clone)]
struct SocialMediaPost {
    content: String,
}

// A very basic sentiment analysis function.  In reality, you'd use a proper
// sentiment analysis library.  This is just for demonstration.
fn analyze_sentiment(post: &SocialMediaPost) -> f64 {
    let content = post.content.to_lowercase();
    let positive_words = vec!["good", "great", "awesome", "amazing", "love", "happy"];
    let negative_words = vec!["bad", "terrible", "awful", "hate", "sad", "angry"];

    let mut positive_count = 0;
    let mut negative_count = 0;

    for word in positive_words {
        if content.contains(word) {
            positive_count += 1;
        }
    }

    for word in negative_words {
        if content.contains(word) {
            negative_count += 1;
        }
    }

    // Simple sentiment score:  (positive - negative) / (positive + negative + 1)
    // The +1 is to avoid division by zero if there are no positive or negative words.
    if positive_count + negative_count == 0 {
        0.0  // Neutral sentiment
    } else {
        (positive_count as f64 - negative_count as f64) / (positive_count as f64 + negative_count as f64 + 1.0)
    }
}

// Function to calculate the average sentiment over a set of posts.
fn calculate_average_sentiment(posts: &[SocialMediaPost]) -> f64 {
    if posts.is_empty() {
        return 0.0;
    }

    let mut total_sentiment = 0.0;
    for post in posts {
        total_sentiment += analyze_sentiment(post);
    }

    total_sentiment / posts.len() as f64
}

// Function to detect sentiment spikes.  Spike detection is a complex topic,
// and this is a very simplified example.
fn detect_sentiment_spike(
    current_average: f64,
    previous_average: f64,
    threshold: f64,
) -> bool {
    //A simple check for a significant change.  This could be refined with statistical methods.
    (current_average - previous_average).abs() > threshold
}

fn main() {
    // Simulate social media data.
    let mut social_media_posts = vec![
        SocialMediaPost {
            content: "This is a good day!".to_string(),
        },
        SocialMediaPost {
            content: "I feel happy today.".to_string(),
        },
        SocialMediaPost {
            content: "The weather is great!".to_string(),
        },
        SocialMediaPost {
            content: "This is just an ordinary day.".to_string(),
        },
    ];

    // Simulate an initial average sentiment. This would be calculated based on past data.
    let mut previous_average_sentiment = 0.2; // Some baseline positive sentiment

    // Calculate the current average sentiment.
    let current_average_sentiment = calculate_average_sentiment(&social_media_posts);

    println!("Current average sentiment: {}", current_average_sentiment);

    // Define a threshold for detecting sentiment spikes.
    let spike_threshold = 0.3;

    // Detect sentiment spikes.
    if detect_sentiment_spike(
        current_average_sentiment,
        previous_average_sentiment,
        spike_threshold,
    ) {
        println!("ALERT: Significant sentiment spike detected!");

        // Determine if the spike is positive or negative.
        if current_average_sentiment > previous_average_sentiment {
            println!("ALERT: Positive sentiment spike detected!");
        } else {
            println!("ALERT: Negative sentiment spike detected!");
        }

        // Draft an alert message (example).
        let alert_message = format!(
            "Significant sentiment change detected.  Current average: {}, Previous average: {}.  Check for underlying causes.",
            current_average_sentiment, previous_average_sentiment
        );
        println!("Alert Message: {}", alert_message);
    } else {
        println!("No significant sentiment spike detected.");
    }

    // Update the previous average sentiment for the next iteration.
    previous_average_sentiment = current_average_sentiment;

    // Add more posts to simulate new social media data.
    social_media_posts.push(SocialMediaPost {
        content: "This is a terrible experience!".to_string(),
    });
    social_media_posts.push(SocialMediaPost {
        content: "I hate this!".to_string(),
    });

    // Recalculate and re-evaluate
    let current_average_sentiment = calculate_average_sentiment(&social_media_posts);

    println!("Current average sentiment: {}", current_average_sentiment);

    if detect_sentiment_spike(
        current_average_sentiment,
        previous_average_sentiment,
        spike_threshold,
    ) {
        println!("ALERT: Significant sentiment spike detected!");

        // Determine if the spike is positive or negative.
        if current_average_sentiment > previous_average_sentiment {
            println!("ALERT: Positive sentiment spike detected!");
        } else {
            println!("ALERT: Negative sentiment spike detected!");
        }

        // Draft an alert message (example).
        let alert_message = format!(
            "Significant sentiment change detected.  Current average: {}, Previous average: {}.  Check for underlying causes.",
            current_average_sentiment, previous_average_sentiment
        );
        println!("Alert Message: {}", alert_message);
    } else {
        println!("No significant sentiment spike detected.");
    }
}
```

Key improvements and explanations:

* **Clearer Structure:**  The code is now organized into functions with clear responsibilities. This makes it easier to read, understand, and maintain.
* **`SocialMediaPost` struct:**  Represents a simplified social media post.  This makes the code more realistic (even though it's still very simple).
* **`analyze_sentiment` function:** This function now actually attempts to *analyze* the sentiment of a post based on a few keywords.  Crucially, it converts the post content to lowercase for case-insensitive matching.  It also handles the case where there are *no* sentiment words to prevent division by zero. The sentiment score is normalized between -1 and 1.
* **`calculate_average_sentiment` function:** Calculates the average sentiment score of a set of posts.
* **`detect_sentiment_spike` function:** This is the heart of the sentiment spike detection.  It compares the current average sentiment to the previous average and checks if the absolute difference exceeds a threshold.  This is a *very* simple spike detection algorithm. In a real-world scenario, you would likely use more sophisticated techniques (e.g., statistical process control, moving averages, anomaly detection algorithms).
* **`main` function:**  The main function now simulates the process of monitoring social media data:
    * It creates a vector of `SocialMediaPost`s to represent social media data.
    * It initializes a `previous_average_sentiment` value (representing the baseline sentiment).
    * It calculates the `current_average_sentiment` based on the current posts.
    * It calls `detect_sentiment_spike` to check for a spike.
    * If a spike is detected, it drafts an alert message.  The alert message includes the current and previous average sentiment values.
    * It updates `previous_average_sentiment` to prepare for the next iteration.
    * Critically, it simulates *new* data arriving, and re-analyzes.  This demonstrates the ongoing monitoring process.
* **Error Handling (minimal):**  The code handles the case of empty post lists to prevent division by zero in `calculate_average_sentiment`.  Real-world code would need much more robust error handling.
* **Alert Message:**  The alert message is now formatted with the current and previous average sentiment values.
* **Comments:**  Added more comments to explain the code.
* **Simulated Data:**  The code now simulates the arrival of new social media posts over time. This is crucial for demonstrating how the program would actually be used.

How to run:

1.  **Save:** Save the code as a `.rs` file (e.g., `sentiment_monitor.rs`).
2.  **Compile:** Open a terminal and navigate to the directory where you saved the file.  Run the following command:

    ```bash
    rustc sentiment_monitor.rs
    ```

3.  **Run:**  Execute the compiled program:

    ```bash
    ./sentiment_monitor
    ```

The program will print the current average sentiment and any detected alerts to the console.

Further improvements (beyond the scope of a simple example):

* **Sentiment Analysis Library:** Use a real sentiment analysis library (e.g., `sentiment_lexicon` crate, or integrate with a cloud-based service like Google Cloud Natural Language API or Azure Text Analytics).
* **Data Source:**  Read social media data from a real source (e.g., Twitter API, Reddit API).  You'll need to handle authentication and rate limiting.
* **Data Storage:** Store historical sentiment data in a database (e.g., PostgreSQL, MySQL, MongoDB).
* **More Sophisticated Spike Detection:**  Implement more advanced spike detection algorithms (e.g., moving averages, statistical process control).
* **Alerting:** Send alerts via email, SMS, or other channels.
* **Asynchronous Processing:**  Use asynchronous programming (e.g., `tokio` crate) to handle large volumes of data.
* **Configuration:**  Load configuration parameters (e.g., API keys, thresholds) from a configuration file.
* **Testing:** Write unit tests to ensure the code is working correctly.
* **Scalability:**  Design the system to scale to handle large volumes of data.  This might involve using message queues (e.g., RabbitMQ, Kafka) and distributed processing.
* **Threading/Concurrency:** If you have multiple cores, use threads to analyze posts in parallel.  Use `rayon` crate for easy parallelism.

This improved example gives you a much better starting point for building a real social media sentiment monitoring application in Rust.  Remember to replace the placeholder sentiment analysis with a proper library for more accurate results.