How Apps Like Tagboard Retrieve Hashtags from Social Media Platforms: Decahose vs Firehose

In today's digital age, social media platforms like Facebook, Twitter, and Instagram are essential tools for engagement and communication among users. Applications such as Tagboard play a vital role in aggregating and visualizing user-generated content from these platforms. One common task is retrieving hashtags that users share, which is achieved through different methods, such as the Decahose and Firehose services from Twitter. This article aims to explain how Tagboard and similar apps retrieve hashtags from major social media platforms and the differences between the Decahose and Firehose services.

Understanding Decahose and Firehose in Social Media Streaming

For streaming text data from Twitter, GNIP/Twitter offers two options for developers and companies:

The Decahose is the standard streaming API. Although it has many limitations, it is free. This is the reason why in 2011, over 1,000,000 apps integrated with Twitter's streaming API. This number has only increased since then. The Decahose provides a sample of all the Tweets associated with a search but aims to provide the most meaningful and least spammy sample. The sample size hovers around 10 percent of the total Tweets available, hence the name Decahose (10 in decimal).

The Firehose, on the other hand, is a 100 percent representation of Twitter’s Tweets and is solely provided by GNIP. However, it is enormously expensive. Batches can contain tens of thousands or millions of Tweets, and prices start at $10,000. This data is in bulk form and requires significant storage and processing capacity to handle the large amount of data.

How Tagboard Retrieves Hashtags

Unless a client specifically requests that Tagboard pull a Firehose query, they guarantee that they are only using the Decahose. Integration with the Decahose is preferred due to its cost-effectiveness and wide availability. For instance, applications like Tagboard normally use the Twitter streaming API to gather a consistent and meaningful subset of the total Tweets available. This method is scalable, cost-effective, and allows for real-time data retrieval and processing.

Given the free nature of Decahose, Tagboard can efficiently process and visualize Twitter data in real-time. The platform filters and aggregates hashtags to provide users with the most relevant and recent content. By leveraging the Decahose, Tagboard ensures a balance between data richness and cost.

Real-time data processing is critical for applications like Tagboard, as it provides the most up-to-date and valuable content for users. The Decahose is perfectly suited for this role, offering a reliable and cost-effective solution.

The Challenges of Using Firehose

While the Firehose offers a complete representation of Twitter’s data, it comes with significant challenges. The high cost and the requirement for substantial resources to handle the large volume of data make it less practical for most applications, including Tagboard. Companies can only afford to embed Firehose costs into their business models when they charge on a per-Tweet basis, as with the case of certain ad tracking or data analytics services.

Furthermore, the Firehose is not suitable for real-time applications. The latency of the service, estimated to be around 15 minutes, means that the data might not be current enough for fast-paced applications. Additionally, handling such a large volume of data requires considerable technical expertise and infrastructure, which can be a hurdle for many businesses.

Conclusion

In conclusion, apps like Tagboard use the Decahose for retrieving hashtags from social media platforms, primarily Twitter. The Decahose offers a free, scalable, and reliable solution for real-time data processing, making it an ideal choice for applications like Tagboard. It allows for the efficient aggregation and visualization of user-generated content, providing a rich and meaningful subset of the total Tweets available. While the Firehose provides a full representation of Twitter’s data, its high cost and technical requirements make it less practical for most applications, including Tagboard, which relies on the cost-effective and scalable Decahose for its core functionality.