NOTE: this code is for a previous version of the Twitter API and I will not be updating in the near future. If someone else would like to, I'd welcome that! Feel free to ping me. END NOTE. Here I define a Tweet listener that creates a file called 'tweets.txt', collects streaming tweets as .jsons and writes them to the file 'tweets.txt'; once 100…

tweet_listener.py

class MyStreamListener(tweepy.StreamListener):
    def __init__(self, api=None):
        super(MyStreamListener, self).__init__()
        self.num_tweets = 0
        self.file = open("tweets.txt", "w")

    def on_status(self, status):
        tweet = status._json
        self.file.write( json.dumps(tweet) + '\n' )
        self.num_tweets += 1
        if self.num_tweets < 100:
            return True
        else:
            return False
        self.file.close()

    def on_error(self, status):
        print(status)

I’ve worked with similar scripts before, and handling rate limits and API errors is really important for something like this. Adding proper logging also helps a lot when debugging issues later. I used a similar setup when integrating data feeds into a website, and small tweaks made it much more stable.

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This looks like a Twitter streaming listener script where tweets are collected in real-time and saved into a file, which is useful for data analysis or building datasets. Since it’s based on an older Twitter API version, it might need updates to work properly with the current API changes.
In general, handling and structuring data efficiently is important in many digital projects, especially when building user-focused platforms. I’ve seen similar emphasis on structure and user experience in different industries too, like on modern lifestyle experience platforms such as Socio HK, where everything feels well-organized and focused on delivering a smooth experience.

Working with older Twitter API code like this is still useful for understanding how streaming data collection and listeners work, especially for learning or maintaining legacy projects. Writing tweets to a file in real time is a simple but effective approach for data logging and analysis.
Even in completely different industries, structured data and presentation matter a lot—similar to how GOSSiP Hong Kong focuses on delivering a well-organized and engaging user experience through its digital presence and brand identity.