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Five social media features that shaped online culture


In this technical report, we survey some now-common social media features, and examine the way they migrated across multiple social media platforms. We focus on five examples that are so ubiquitous now, that they are embedded in social media user-experience and culture: the internet cookie, the infinite news feed, the hashtag, the “Like” button, and the disappearing image or video. In discussing each feature, we identify the inventor, the origin-story of the feature, and the impact each feature had on the greater social media ecosystem.

Background and methodology

As of 2021, the internet connected 63% of the entire world and 90% of the developing world (Bogdan-Martin, 2021). With that interconnectivity, human communication online has been influenced by large social media platforms and features that were initially designed to enhance online interactions. In other words, while these features are designed by a handful of engineers, they often affect hundreds of millions of users. For example, usage data shows that in 2015 WhatsApp had 900 million active users. Their software engineering team consisted of 90 people (Metz, 2015). 90 individuals shaped the communication of 900 million users, simply by determining in-app protocols and processes.

Software engineering has always been iterative, and this has held true when it comes to social media features. In other words, any new feature unique to one platform is rapidly copied and improved on by engineers at other companies. If one platform’s unique feature is a competitive enough advantage, other companies will rapidly integrate it into their own services. In this way, these cumulative, common features spread across multiple platforms, to influence online spaces, behaviors and expectations, regardless of the platform they’re on at that particular moment.

In this report we zoom in on five social media features, and delve into their background. We discuss the individual or team that created the feature, then chart how that specific feature spread across the internet. Each section concludes with a brief discussion of how the features have transcended geographical and linguistic boundaries to create and shape aspects of online culture.

To conduct this inquiry, we use social media analytics and historical data from Infegy Atlas, including a dataset that goes back to 2007. We supplement our historically contextualized data with numerous other archival sources. In this report, we organize our findings into five, chronological key features, each of which created their own online cultural paradigm:

  • Netscape’s browser cookie - personalizing content for users
  • Facebook’s News Feed - curating individualized experiences
  • Twitter’s hashtag - enabling user-discovery and aggregation of content
  • Facebook’s Like button - promoting virality and online tracking
  • Snapchat’s disappearing media - pointing to the future of social media

1. Netscape’s browser cookie - personalizing content for users (1994)

History of the browser cookie

Lou Montulli, a 23-year-old junior engineer for Netscape – the world’s first internet browser – invented the cookie in 1994. He was attempting to solve a problem he called “website Alzheimers” (Montulli, 2013). Ecommerce was still in its infancy, and the Netscape development team was developing what we now all call a shopping cart. This “shopping cart” which would allow users to add and remove items to the “shopping cart” before proceeding with their purchase. However, Netscape had no way to remember each item. Montulli realized that while users could log in to a site, immediately after they clicked away – even to another page on the same site – the site would “forget” who they were. For example, if a user logged into a banking website, then clicked to check their balance, they would have to log in again, because the website’s server had no way to identify who they were after they navigated away from the original page. He created the “internet cookie” to address this issue.

Browser cookies are small text files that serve as an internet users’ identification to a browser. After the user logs into a website, the website sends a cookie, or a very small text file made up of a key-value pair. This pairing has a unique identifier that calls back to the initial website, pseudo-anonymously identifying the specific user. The original cookie did not track user-activity, but only established the user’s identity. This allows the website to “remember” the user, and enables the majority of now-commonplace features on the internet, namely journeying through a website or platform without having to log in at every interaction.

After Montulli’s initial cookie, the feature rapidly spread across the internet and quickly became a foundational piece of online technology. Internet Explorer, then a rapidly expanding internet browser, developed its own cookie in October 1995. By 2001, a New York Times article wrote that “most business Web sites [sic] now use cookies (including the sites of The New York Times Company)” (Schwartz, 2001). Cookies, and the website memory they facilitated, came to shape the entire internet and user-experience on websites. Here, we focus on the impact of cookies on social networking.

Impact of the cookie on social networks

At their face value, cookies allow users to browse to their friends’ profiles, read messages, or glance through photos, without logging in every time they clicked on a new page. From a broader perspective, they allowed the continuity of one’s online entity. In other words, cookie-facilitated website memory allowed users to post and interact with websites transparently (as opposed to as anonymous users). This enabled the explosion of social media features that would follow – anything from sending a message, to “liking” content, to browsing other profiles – in which an identifiable user, linked to a specific account, could create a documentable online presence. Facebook’s Real Name Policy was entirely reliant on the function of cookies, and this policy in itself set culture-shaping ripples in motion (to learn more about this, refer to “The Evolution of Social Media Part II: From Public, Centralized to Private, Decentralized Social Networks”).

However, very soon after their launch, internet cookies were engineered to track users’ movements to and around third-party websites. This was a more controversial feature, and raised a multitude of concerns regarding privacy. Montulli was aware that cookies could potentially be used in this way, noting, “I agonized about the decision for weeks and in the end I chose to keep them” (Montulli, 2013). His concerns were realized.

Almost immediately after the launch of Monutulli’s original cookie, advertising companies realized the monetary value in building profiles around users’ browsing habits. Cookies soon became the foundation of how the modern advertising industry interfaced with social media platforms, and the funding avenue for social media platforms. DoubleClick, an online advertising company, became one of the first movers around monetizing user behavior data. (Figure 1, Infegy Atlas - Topics Around DoubleClick, 2022).

Word Cloud of Topics Related To DoubleClick
Figure 1: Word Cloud of Topics Related To DoubleClick. Note the presence of "tracking"; Infegy Atlas data.

Founded in 1995, DoubleClick built its entire business on tracking users via third-party cookies, bundling that data, then selling it to advertisers. Google acquired DoubleClick in 2008 in a $3.7 billion deal, in what New York Times journalist Steve Lohr called “a total game, a crucial piece in the larger jigsaw puzzle” (Lohr, 2020). Acquiring DoubleClick allowed Google to monetize its search business (Figure 2, Infegy Atlas - DoubleClick Atlas Trends Peaking in 2008, 2022). Now, social media and tech companies like Google and Facebook make the vast majority of their operating revenue from advertising. In 2021, Facebook made 97% of its operating revenue from advertising, while Google made 81.3% of its operating revenue from advertising (Statistica, 2021).

Trend Graph showing DoubleClick peaking in 2008 during the Google Acquisition
Figure 2: Trend Graph showing DoubleClick peaking in 2008 during the Google Acquisition; Infegy Atlas data.

Cookie-enabled profiles didn’t simply allow social media companies to monetize users by serving up targeted advertising. They facilitated a whole new feature that was designed to keep users on social media websites and applications. We discuss this feature and its implications below.

2. Facebook’s News Feed (2006)

The News Feed is a live, automatically-updating, infinite feed that shares a user’s activity around the rest of their social network. Its base functionality is facilitated by data originally collected from internet browser cookies. The process for selecting and serving content has evolved over time. Now, highly evolved algorithms are engineered to continuously record user behavior and interests, and serve engaging content.

The history of the News Feed

Prior to the development of the News Feed, Facebook’s original intention for itself was to be an online phonebook where a user could learn more about the people around them. It actually billed itself as an “an online directory that connects people through social networks at colleges'' (Zuckerberg, 2004). Its original network structure was designed to this specific purpose. For example, each time a user wanted to look up what their friend was up to, they had to physically browse to that page. In other words, Mark Zuckerbeg’s original iteration of Facebook resembled a phone directory – a series of static pages, with limited opportunities for interactivity. In 2006, Ruchi Sanghvi, Facebook’s first female engineer, and the rest of her team changed that paradigm.

Sanghvi’s team designed a system that fetched every user update or interaction, then broadcast that activity on a new, infinitely populating, dedicated area of the home page – the News Feed. The News Feed automatically broadcasted users’ activities to their entire social network. Like the original browser cookie, the initial News Feed algorithm was simple, non-invasive and served a single purpose – connecting users to information about their friends. For example, if someone got tagged in a photo, that picture would be the initial piece of content they saw (Ghaffary, 2022). If a user friended another, liked a page, or posted a photo, those updates would appear on each other's friends' feed.

Sanghvi said in a 2022 podcast, The Land of Giants, that “it was one of the largest distributed systems ever built,” and quipped that the young engineers didn’t realize what they were building (Ghaffary, 2022). Sanghvi’s News Feed led to an entire site re-design for Facebook, the launch of which received mixed responses.

While the algorithm itself was simple and non-invasive, Facebook’s early users viewed the feature itself as a violation of privacy. Infegy Atlas data shows that 2007-2008 Facebook users complained repeatedly around the initial implementation of the feature, especially the privacy violations (Infegy Atlas - Initial Reactions to Facebook’s News Feed, 2022). In fact, Infegy Atlas tracks these upswings in negative sentiment each time Facebook released a new feature or updated their News Feed over the next decade (Figure 3, Infegy Atlas - Facebook News Feed Sentiment Over Time, 2022). The evolution of the News Feed continued to fuel controversy, especially as the algorithm eventually was tuned to measure and rate user-engagement, and push content (including other user-made content) towards users based on their activity on the platform.

Sentiment trend graph showing a persistent decline in sentiment around conversation relating to Facebook's feed
Figure 3: Sentiment trend graph showing a persistent decline in sentiment around conversation relating to Facebook's feed; Infegy Atlas data.

These user protestations grew into Facebook groups with hundreds of thousands of angry users. Almost immediately after the News Feed was released in 2006, Zuckerberg was forced to release an apology. Zuckerberg wrote an open letter on the platform, declaring, “We really messed that one up” (Cashmore, 2006).

The algorithmic “fetching technology” behind Facebook’s News Feed rapidly became pervasive across the internet, and shaped the social media apps that followed. Instagram launched in 2010 with a chronologically sorted feed. In 2016, two year after Facebook acquired it, Instagram transitioned to an algorithmically sorted feed (Lua, 2021). The infinitely scrolling, algorithmically-generated feed now powers almost every social network on Earth including YouTube, Instagram, Pinterest, and Reddit.

The impact of the News Feed on social media

The News Feed had two major impacts. The first of these was structural as the engineering behind the News Feed mandated a change from a directory-style site to a dynamic network. The second impact posed a conceptual change to how users interacted on social media platforms. We discuss this below.

Social media scholar Sangeet Choudhary suggests that the News Feed shifted the concept of a social network from a “user-centered activity” to a “network-centered activity” (Choudhary, 2021). Prior to the News Feed, user activity centered around other users. After its introduction, user activity became influenced by the very network that they were a part of. By choosing which content to serve to users, Facebook and the other social media companies that followed, enabled algorithmic-amplification of information, and thus allowed for users to create and view viral content. In 2017, tech journalist Farhad Manjoo noted that over 2 billion people, monthly, view content served up by Facebook’s algorithmic curators (Manjoo 2017).

Social media’s change to a network-centered activity, driven by the adoption of the News Feed, has changed almost every industry on the planet. An exemplar of this industry-metamorphosis of this is how the News Feed impacted journalism.

After the News Feed’s release, Facebook users began reading and finding news online. This percentage expanded all the way up to 2018, where a Pew Research Poll found that 55% of American adults got their news from Facebook or Twitter feeds (Pew Research, 2018). This led to the first massive upheaval in traditional, print journalism: it ushered in a new era of non-traditional, online news media like Vox, Vice News, Buzzfeed, etc. This eventually led to profit cuts for legacy newspapers, especially those that told local stories.

Second, as people were served more algorithmically-generated content, the time they spent reading even online text-based content dropped. Journalist Nicole Martin writes in Forbes that news readers spend an average of just 15 seconds reading an article (Martin, 2018). Infegy Atlas data shows a similar trend with changes assorted with the average post length, where users are posting shorter and shorter textual content over the last 15 years (Figure 4, Infegy Atlas - Graph of Document Length, 2022). This was further exacerbated by Facebook’s “pivot to video,” a social media paradigm-shift that led to the lay-offs of hundreds of traditional print-media journalists.

Figure 4 - Average Words Per Post
Figure 4: Average words per post, analyzing billions of collected posts over the last 15 years. People posting online have use persistently fewer words with each post; Infegy Atlas data.

Ultimately, these two New Feed-perpetuated upheavals led to the metamorphosis of journalism as an industry. Even online journalism continues to move away from long-form articles to quick-read, headliner content and click-bait designed for online audiences.

3. Twitter’s Hashtag (2007)

The hashtag is a way for users and social networks to label, categorize, and share content. The pound sign (also called hash sign) acts as a metadata tag, and allows computers to easily isolate the following text. This isolation or flagging accomplishes two things from the perspective of the user. When included within a post, it allows users to tag and categorize that piece of content as the user desires. Second, it allows users to discover related content based on the self-described hashtags used by other users.

The history of the hashtag

The pound/hash sign has been used as a unique data identifier for as long as computers have been around. They were first introduced in social networking within Internet Relay Chats, commonly known as IRCs, to identify channels. These chats were created in 1988 to facilitate text-based instant messaging. In this context, the “#” signified that the next word was the name of the channel within the chat. For example, the chat dedicated to computing would be marked, “ #computing.”

Inspired by Internet Relay Chats, Chris Messina introduced the use of the hashtag on Twitter in 2007 (Figure 5, Picture Of First Tweet with a Hashtag, 2007). Twitter was in its infancy, and Messina devised an idea to use the hash sign in posts to group and organize content so that posts with related content could be easily searchable. His intention was that users could intentionally tag and categorize their posts. Messina even pitched the idea of “hash tagging” posts to Twitter and presented a template of what the tagging channels could look like. The Twitter team did not get on board; nonetheless, Messina pushed its use with his Twitter followers, and it grew organically (BBC, 2014). He did not patent its use or make any money off what soon became a social media mainstay.

Screenshot of Chris Messina's original tweet
Figure 5: Screenshot of Chris Messina's original tweet where he first proposed the hashtag.

Noting the growing popularity of hashtags on the platform, Twitter quickly integrated hashtags into its platform. The company released “Trends” in 2008 which automatically collected the top performing hashtags into a single pane that users could quickly scan.

Soon after hashtags became officially supported by Twitter, the feature was picked up by other social networks: YouTube, Gawker, Facebook all developed hashtag integrations into their platforms (Warren, 2013). The feature is now included, by default, with almost every social network from TikTok to Pinterest. As of today, hashtags are commonplace around the internet, frequently appearing in posts all over the internet. (Figure 6, Atlas Data - Average Number of Hashtags Per Post By Year, 2022)

\How hashtag usage has changed over time
Figure 6: How hashtag usage has changed over time; Infegy Atlas Data.

The significance of the hashtag

From unseating media moguls to toppling Middle Eastern dictatorships, hashtags have already made an undeniable impact on society. In this section, we delve into their significance, focusing on two of the most impactful uses of hashtag activism: their use during the 2010 Arab Spring, and how the movement in the United States pushed a reckoning of accountability for the perpetrators of sexual violence.

#ArabSpring was the first mass-movement enabled by hashtag activism; it peaked in 2011. Regionwide, pro-democracy, protests started in the Middle East and Northern Africa after Tunisian shopkeeper Mohamed Bouazizi lit himself on fire in front of a government building protesting the anti-democratic regime. While Chris Messina’s internet was intended to group related content, and facilitate the search around relevant topics, in this case, pro-Democracy activist used the hashtag #ArabSpring in ways beyond his imagination and intent. The hashtag allowed them to coordinate marches, share intelligence, and warn each other of arrests – all through the medium of the internet. Despite English not being their first language, protesters used the hashtag #ArabSpring most frequently.

We used Infegy Atlas to track the Arab Spring movement online, and found that post volume around Arab Spring related topics was clustered within the regions that experienced the most upheaval. (Figures 7 and 8, Infegy Atlas Data - #ArabSpring Middle Eastern and African Map, 2022) Ultimately, Arab Spring, and the hashtag activism that enabled it played a vital part in overturning the Tunisian government, triggering the Syrian Civil War, and prompting the modernization of the Saudi Arabian government.

Countries with high post volume
African countries with high post volume
Figures 7 and 8: Maps showing the Asian and African countries with the highest post volume of #ArabSpring related conversation; Infegy Atlas data.

Hashtags have also held powerful people accountable in the United States, with the #MeToo being the most prominent example. While “Me Too” in the context of sexual assault first arose on mySpace in 2006, its popularity exploded in 2017 with actress Alyssa Milano encouraging women to reply to a tweet with “#MeToo” if they had become victims of sexual assault (Figure 9, Original “Me Too Tweet”).

Figure 9 - First MeToo Tweet
Figure 9: Screenshot of Alyssa Milano's original tweet credited with launching the #MeToo Movement.

The #MeToo movement’s goal was twofold: to encourage women to speak out about the abuses they had suffered, and hold predatory men accountable. Infegy Atlas data shows over 63 million mentions, suggesting the first goal was achieved. (Figure 10, Infegy Atlas Data - #MeToo Trend Graph, 2022). Manhattan District Attorney Cyrus Vance led the charge towards the second goal by securing a 2020 trial conviction against Harvey Weinstein, the most notorious of the predators uncovered by the movement.

Trend graph showing post volume attached to #MeToo.
Figure 10: Trend graph showing post volume attached to #MeToo. The first peak reflects the emergence of the hashtag while the second peak reflects Harvey Weinstein's indictment; Infegy Atlas data.

#MeToo had not only an impact in the US, but all over the world. Infegy Atlas shows that a large proportion of hashtags are associated with #MeToo movements in other countries. Infegy research has already studied the movement’s prominence in Belarus (for details, download our Gen Z Personas Technical Report), but #MeToo also has an impact in India, Korea, and Spanish-speaking countries. (Figure 11, Infegy Atlas Data - #MeToo Hashtag Word Cloud, 2022).

Word cloud showing hashtags affiliated with #MeToo
Figure 11: Word cloud showing hashtags affiliated with #MeToo. Note the international and non-English hashtags; Infegy Atlas data.

While the Arab Spring and the Me Too Movement have unquestionably positive outcomes, hashtag’s impacts on the world have not been universally good. Since hashtagging is user-generated it is essentially an unmonitored cataloging system; it collects and categorizes both true and false information into the same buckets of “related” content. Technologist Abby Ohlheiser of the MIT Technology Review wrote in July of 2022 that Twitter’s Trends page was ground zero for algorithmic amplification of falsehoods online (Ohlheiser, 2022). Suffice it to say that the combination of user-intent and machine-learning has led to both positive and harmful cultural impact.

4. Facebook’s Like button (2009)

The Like button allows social media users to quickly express interest in a piece of content without investing the time to leave a written comment. It also allows users to save those pieces of content in a centralized location so they can refer to them later.

The history of the Like button

The Like button first originated on digg in 2004, followed by Vimeo in 2005. Early Facebook software engineers worked on their version but took a few years to get off the ground.

Discussion around a potential “Awesome Button” for Facebook began around July of 2007, but it took until 2009 for the feature to be released. Andrew Bosworth, one of Facebook’s first 15 software engineers – now the Chief Technical Officer at Meta – called it a “cursed project” (Bosworth, 2015). Justin Rosenstein is credited with the invention, brainstorming several ideas like rating a post with stars, a plus sign, before eventually settling on a thumbs up icon (Banton, 2021).

Facebook launched the feature in February of 2009. In the blog post promoting the new feature, Leah Perlman described the Like button as an “easy way to tell friends that you like what they’ve shared on Facebook with one easy click” (Pearlman, 2009). She goes on to mention how Facebook believed likes would be an easier way to interact with posts versus comments. If a user wanted to interact with a post, but didn’t want to invest the time to write an entire comment, he or she could just leave a like.

The Like button gained traction with users quickly. Infegy Atlas shows a 416% increase in post volume from when Facebook announced the feature in 2009 to when mentions of the feature peaked in 2011 (Figure 12, Infegy Atlas - Mentions of the Like Button and Reactions). The concept of the Like button rapidly spread across the internet from this point onward: YouTube scrapped its five star rating system in 2010 for the thumbs up button, and Google+ launched in 2011 with a way for users to +1 content. Every social media network now launches with some feature that allows users to Like, favorite, or upvote posts.

Trend graph showing the initial release of the Facebook like button
Figure 12: Trend graph showing the initial release of the Facebook like button and their subsequent release of Facebook Reactions; Infegy Atlas data.

Facebook further innovated its original Like button by releasing Reactions in 2017. Reactions allowed users to attach more diverse emojis to posts instead of just using a Thumbs Up emoji. Facebook has expanded the Like button to include other reactions like Love, Angry, Haha, and, during the early days of the COVID-19 Pandemic, included Cares.

While Facebook’s reactions expanded on the concept of the Like button, they didn’t attract the same level of discussion and reaction. Other social networks later copied this feature. For example, LinkedIn allows users to respond with emojis indicating Good Idea, Celebrate, Love, and others.

The significance of the Like button

The Like button changed the social media and internet advertising industry in three ways.

First, the Like button turned the expression of personal interests from a private emotion to a public gesture of approval or support. This concept is related to one we discussed above – Facebook’s News Feed. Namely, each Like that a user gave could be blasted out to their entire social network. Facebook used the data from Likes and later Reactions to feed into its content curation algorithm, in order to push the posts with the highest engagement to the top of user’s feeds.

First, the Like button turned the expression of personal interests from a private emotion to a public gesture of approval or support. This concept is related to one we discussed above – Facebook’s News Feed. Namely, each Like that a user gave could be blasted out to their entire social network. Facebook used the data from Likes and later Reactions to feed into its content curation algorithm, in order to push the posts with the highest engagement to the top of user’s feeds.

Second, the Like button enabled Facebook to amplify the existing tracking effects of third-party cookies. Facebook accomplished this by enabling third-party web developers to embed Like buttons in their websites with just one extra line of code. These Like buttons on external sites acted as tracking “beacons,” so when logged-in users visited those sites, Facebook would get notifications around their browsing patterns. Facebook built even-more detailed advertising dossiers on its users, which it used to sell target advertisements too. In short, Facebook’s Like button proved to be a crucial piece in its monetization engine.

Third, the Like button represented the first meaningful step towards the commodification of reactions, views, and engagements. The Like button added a quantifiable measure of post engagement and views. This measure became a way to calculate monetization in online advertising, and thus allowed online marketers to estimate how much reach their campaigns were getting. On the content creation side, the focus on Likes became the first step towards social media’s influencers (Figure 13, Infegy Atlas - Growth of the Influencer, 2022). While the term “Influencer” would take a few more years to develop, their rise would not have been possible without the Like button.

Trend graph showing the explosive growth of the term "Influencer"
Figure 13: Trend graph showing the explosive growth of the term "Influencer"; Infegy Atlas data.

5. Snapchat’s Disappearing Image (2011)

The Snap, or disappearing image, is the cornerstone feature of Snapchat where users can post or share an ephemeral image; it exists online for a customizable period of time before it disappears from the internet forever.

The history of the Snap

By 2011, Facebook, Twitter, and other legacy social media companies had been around for almost a decade. The billions of posts these platforms contained slowly created an embarrassing timeline that began to follow users to unfortunate places like job interviews and background checks, often with damaging results to professional goals and relationships.

Reggie Brown, Evan Spiegel, and Bobby Murphy sought to end this pattern by starting Snapchat in September 2011. They invented the Snap, a disappearing image that you could send to specific friends. They added support for video a year later in December 2012. The next year, Snapchat created a feature called Snapchat Story which allowed users to string together a series of Snaps. Snapchat Stories essentially functioned as a more immersive, video-form version of a status on Snapchat, and was very popular.

Snaps soon became so popular that users began migrating to Snapchat from other large social platforms. It also began to attract the new generation of users joining social media platforms. Snapchat’s meteoric takeover of the younger generation of users led Facebook to view it as an existential threat, and was motivated to create a strategy to attract younger users.

Facebook enacted this strategy through Instagram, which they had acquired by this time. Facebook integrated the Story feature into Instagram, and their iteration ended up being a ruthless copy of the Snapchat Snap, built for Instagram. Instagram had already acquired a larger user base than Snapchat and the new copycat feature became very popular with its current users, curtailing the bleed-off of users toward Snapchat (Figure 14, Infegy Atlas Graph on SnapChat + Instagram + TikTok Post Volume, 2022). Then-Instagram CEO Kevin Systrom was open about ripping-off the feature, telling TechCrunch that Snapchat “deserved all the credit” (Kantrowitz, 2020).

Trend graph showing the growth of Snapchat and Instagram Stories
Figure 14: Trend graph showing the growth of Snapchat and Instagram Stories; Infegy Atlas data.

Facebook’s strategy worked at the cost of Snapchat: Instagram “slowed Snapchat’s growth considerably, and destroyed billions of dollars of value in the parent company, Snap Inc.” (Kantrowitz, 2020). A trend comparison on Infegy Atlas showcases Instagram’s post volume essentially engulfing Snapchat’s: mentions of Instagram Stories were 5.8 times higher than mentions of Snapchat stories by December 2017 (Figure 15, Infegy Atlas Graph on SnapChat vs. Instagram Stories, 2022).

Trend graph showing how people mentioning SnapChat on social media
Figure 15: Trend graph showing how people mentioning SnapChat on social media almost overtook those mentioning Instagram on social media in 2016, Infegy Atlas data.

The disappearing image or video, pioneered by Snapchat is now all over social media. Twitter experimented with Fleets, a SnapChat Story clone before shutting it down in 2021 (Esposito, 2021). TikTok is now experimenting with temporary “stories” in 2022. Nylon Magazine journalist Sophia June predicts that their implementation of the feature will “run Instagram into the ground” (June, 2022). With Snaps and Stories now all over the internet, we’ll now pivot to a discussion on their significance.

The significance of the Snap

Snaps and stories changed social media in two ways. First, they paved the way for a more private, more conversational internet. While Facebook’s News Feed automatically broadcasted user content across their entire network, Snapchat allowed users to specifically select who they wanted to Snap. By allowing users to specifically direct who received their content, Snapchat shrunk larger networks into more intimate spaces (See The Evolution of Social Media- Part II for a detailed discussion of user privacy and the move towards decentralized networks).

Second, the conversational Snap and Story enabled Snapchat’s experimentation with live, augmented reality and virtual reality features, namely, filters. Snapchat released these filters in 2015, using advanced AR to allow users to apply live video filters to their Snaps before sending them. This initial adoption ushered in an extensive investment in the AR/VR space, which represents the next frontier for internet media (See Evolution of Social Media Part- I for a discussion of augmented and virtual reality integrations in social spaces).


This paper identified the five key features that shaped the development of social media and the internet. It began with the internet cookie and discussed how that enabled advertising-based tracking. It then mentions how Facebook’s News Feed expanded social networks and created the algorithmic content curation users are accustomed to today. Next, it unpacked how Twitter’s hashtag was invented and told how it toppled governments. Fourth, it explained how the Like button iterated on the cookie-based tracking and paved the way for the commodification of user engagement online. The technical report concluded with a discussion of how Snapchat paved the way for a smaller, more private internet and helped usher in the use of augmented and virtual reality.

These five features, coded by a small handful of Silicon Valley software engineers, changed the course of the 21st Century. Each of them shaped aspects of online cultural behavior, and cumulatively, they created the social media world to which we have become accustomed. While the specific features might evolve with future iterations, what they illustrate is that web applications and software features have the scalability to impact entire industries and human interactions – both online, and off.


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