4 Matching Annotations
  1. Last 7 days
    1. 5.3.2. Social Networking Services# 2003 saw the launch of several popular social networking services: Friendster, Myspace, and LinkedIn. These were websites where the primary purpose was to build personal profiles and create a network of connections with other people, and communicate with them. Facebook was launched in 2004 and soon put most of its competitors out of business, while YouTube, launched in 2005 became a different sort of social networking site built around video.

      It's fascinating how swiftly the "profile + connections + messaging" formula became the norm once these platforms debuted. Facebook's ascent illustrates the power of network effects in social media; when a critical mass of users congregates on a single site, rivals find it tough to gain traction, even with comparable offerings.

    2. In Web 2.0 websites (and web applications), the communication platforms and personal profiles merged. Many websites now let you create a profile, form connections, and participate in discussions with other members of the site. Platforms for hosting content without having to create your own website (like Blogs) emerged. And all of these websites became much more interactive, with updates appearing on users’ screens without the user having to request them.

      This segment illustrates the metamorphosis of the internet, catalyzed by Web 2.0, from a static collection of "pages you visit" to dynamic "places you participate." The integration of communication tools and user profiles into platforms fundamentally reshaped identity and relationships, hence fueling the swift expansion of social features and communities.

  2. Jan 2026
    1. Computers typically store text by dividing the text into characters (the individual letters, spaces, numerals, punctuation marks, emojis, and other symbols). These characters are then stored in order and called strings (that is a bunch of characters strung together, like in Fig. 4.6 below).

      The realization that "text" on social media encompasses more than just English letters, but also spaces, punctuation, characters from diverse languages, and emojis, was significant. Inconsistent encoding or character handling can result in garbled text, inaccurate length calculations, and adverse effects on subsequent text cleaning, keyword statistics, and sentiment analysis. Consequently, preprocessing strings is essential prior to undertaking data analysis.

    2. When computers store numbers, there are limits to how much space is can be used to save each number. This limits how big (or small) the numbers can be, and causes rounding with floating-point numbers. Additionally, programming languages might include other ways of storing numbers, such as fractions, complex numbers, or limited number sets (like only positive integers).

      This got me thinking: numbers aren't kept with perfect accuracy. Memory constraints and the way data types are defined lead to floating-point errors, rounding discrepancies, and the potential for overflow. When we apply this to social media analytics, it means that those "objective" metrics we rely on—likes, retweets, and the like—can also be affected by calculation and display errors. Consequently, we need to factor in these technical limitations when we analyze the results.