Why tweets are limited to 140 characters

LinkedIn is known for being a professional social network, so you can network, build relationships with influencers, become an influencer via its Pulse publishing platform, and easily identify and connect with prospects across industries. And Twitter? People are easily distracted and have short attention spans—in fact, the National Center for Biotechnology Information found that the average attention span is only 8.

A short character count allows marketers to be quick and nimble to react to real-time activities. Consider the flood of messages that erupt whenever anything newsworthy happens or something goes viral. Marketers have adapted to become fast-reacting engines, churning out content within seconds.

Gone are the days when social media messages need to be planned out days or weeks in advance. Tide quickly took advantage of this moment to advertise their Tide Plus ColorGuard detergent, which got an impressive amount of traction.

It also expanded tweets by not counting media attachments against the character limit. Others have gone further — Twitter clone Mastodon launched with character messages.

Still, super-sized tweets are likely to change the nature of the network as more people gain access to them, and in unpredictable ways. What new kinds of jokes and memes are possible at characters that were not possible at ? Will characters allow for just enough nuance and diplomacy that we may be able to avert global thermonuclear war?

Most Twitter applications should already be able to show longer tweets, thanks to changes that the company introduced to its API last year. It will count down from until you run out of room. We are excited to share this today, and we will keep you posted about what we see and what comes next. This is a small change, but a big move for us. Proud of how thoughtful the team has been in solving a real problem people have when trying to tweet. And at the same time maintaining our brevity, speed, and essence!

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Are we turning the corner on Covid treatments? By Kelsey Piper. Sign up for the newsletter Sign up for The Weeds Get our essential policy newsletter delivered Fridays. Thanks for signing up! Check your inbox for a welcome email. For instance, the advent of the telegraph, in which words were literally at a premium, necessitated an elliptic style that has become known as telegram style of telegraphese , which is viewed as a normal expressive form of language Barton, ; Isserlin, ; Tesak and Dittmann, A more contemporary example of an elliptic style is textese , which is often used in modern text messages Drouin and Driver, Textese and telegraphese are both characterized by an imposed limit constraint Barton, ; Drouin and Driver, ; Isserlin, ; Tesak and Dittmann, However, a crucial difference is the nature of the length restriction: In telegrams, the costs are related to the number of words and not the number of characters.

In other words, a cost-effective telegram contains as few words as possible. In text messages, on the other hand, one is obliged to conserve character space, which results in a different practice of economy Frehner, Character reduction as performed in textese, can be achieved not only by minimizing the number of words but also by abbreviating words and using shorter synonyms and symbols.

The character-reducing strategies inherent to textese are referred to as textisms Carrington, ; Lyddy et al. They evolved not only to save character space but also to reduce typing efforts. Textisms reduce character use without compromising the conveyed meaning and even add meaning in some cases.

This includes acronyms e. Another strategy to reduce character usage is the omission of certain part-of-speech POS categories. The SVO structure, comprises pro nouns and a verb. The main components of the SVO structure are unlikely to be omitted. In contrast, the POS categories that modify the basic structure and introduce additional information are more likely to be excluded.

In textese and telegraphese, articles and conjunctions are often excluded Carrington, ; Oosterhof and Rawoens, Consistent with this intuition, eyetracking studies of reading have shown that function words such as articles and prepositions are often skipped in normal reading because these words are both short and highly predictable from context Rayner et al.

A reader can even fill in omitted articles and conjunctions. Although the overall readability is compromised, the message is still clear. Therefore, if words have to be omitted to reduce character usage, they are likely to be function words. However, other words can also be omitted, leaving out information.

In this case, additional information is being withheld. Generally, this means limit constraints might also affect sentence structure.

An example of a contemporary platform that might necessitate elliptic writing strategies is Twitter, an online microblogging platform which imposes a message-length limit to its users. On November 8th , Twitter doubled the character limit from characters to characters Footnote 1 ; we will refer to this as the character limit change CLC. When Twitter announced the upcoming CLC the community responded ambivalently. The doubling of the maximum tweet length provides for an interesting opportunity to investigate the effects of a relaxation of length constraints on linguistic messaging.

What happened to the average length of tweets? And more interestingly, how did CLC impact the structure and word usage in tweets? The need for an economy of expression decreased post-CLC. In addition, we hypothesize that the CLC affected the POS structure of the tweets, containing relatively more adjectives, adverbs, articles, conjunctions, and prepositions.

These POS categories carry additional information about the situation being described, the referential situation; such as features of entities, the temporal order of events, locations of events or objects, and causal connections between events Zwaan and Radvansky, This structural change also entails that sentences will be longer, with more words per sentence. They found that pre-CLC tweets in this character range comprise relatively more abbreviations and contractions, and fewer definite articles.

In the current study, we used a different approach that adds complementary value to the previous findings: we performed a content analysis on a dataset of approximately 1. The dataset comprises Dutch tweets that were created between 25 October and 21 November , in other words two weeks prior to and two weeks after the CLC.

We performed a general analysis to investigate changes in the number of characters, words, sentences, emojis, punctuation marks, digits, and URLs. To test the first hypothesis, we performed token and bigram analyses to detect all changes in the relative frequencies of tokens i. These changes in relative frequencies could then be utilized to extract the tokens that were especially affected by the CLC.

An example of each investigated POS category is presented in Table 1. The data collection, pre-processing, quantitative analysis, figures, token analysis, bigram analysis, and POS analysis were performed using Rstudio RStudio Team, The CLC occurred on 8 November at a.

This period is subdivided into week 1, week 2, week 3 , and week 4 see Fig. To distinguish the CLC effect from natural-event effects, a control comparison was devised: the difference in language usage between week 1 and week 2, referred to as Baseline-split I. Furthermore, the CLC could have initiated a trend in the language usage that evolved as more users became familiar with the new limit.

This trend could be shown by comparing week 3 with week 4, referred to as Baseline-split II. Moving average and standard error of the character usage over time, which shows an increase in character usage post-CLC and an additional increase between week 3 and 4. Each tick marks the absolute beginning of the day i. The time frames indicate the comparative analyses: week 1 with week 2 Baseline-split I , week 3 with week 4 Baseline-split II , and week 1 and 2 with week 3 and 4 CLC.

The website Footnote 2 twiqs. The tweet-ids allow for the collection of tweets from the Twitter API that are older than 9 days i. Non-Dutch tweets, retweets, and automated tweets e. In addition, we excluded tweets based on three user-related criteria: 1 we removed tweets that belonged to the top 0.

All cleaning procedures and corresponding exclusion numbers are presented in Table 2. URLs, line breaks, tweet headers, screen names, and references to screen names were removed.

URLs add to the character count when located within the tweet. However, URLs do not add to the character count when they are located at the end of a tweet. To prevent a misrepresentation of the actual character limit that users had to deal with, tweets with URLs but not media URLs such as added pictures or videos were excluded. The T -test is similar to a standard T -statistic and computes the statistical difference between means i.

Negative T -scores indicate a relatively higher occurrence of a token pre-CLC, whereas positive T -scores indicate a relatively higher occurrence of a token post-CLC. The T -score equation used in the analysis is presented as Eq. N is the total number of tokens per dataset i. This equation is based on the method for linguistic computations by Church et al. The POS tagger operates using a maximum entropy maxent probability model in order to predict the POS category based on contextual features Ratnaparkhi, An ostensible limitation of the current study is the reliability of the POS tagger.

Therefore, we assume there are no systematic confounds. The results comprise three components: 1 General statistics—the CLC induced differences across multiple tweet features, 2 token i. After the CLC, the average tweet length increased. Table 3 contains descriptive information about different tweet features such as character and word count.

This table also provides the absolute and relative differences between pre and post-CLC tweets. All tweet features increased in frequency. Furthermore, the standard deviations of all length features increased, indicating an increase in variability.

This suggests some users took advantage of the additional character space, whereas others continued to use fewer than characters. Figure 1 shows that the average character usage increased immediately after the CLC. In addition, the character usage also increased from week 3 to week 4, suggesting that some users became familiar with the limit in the week after the CLC.

Figure 2 provides an overview of all observations and shows an increase in character usage from pre to post-CLC time frames. Figure 3 displays the character 3a , word 3b , and sentence 3c usage over time, which show a similar increase in tweet length. Figure 4a displays the number of characters per word i. Figure 4b, c present an increase in sentence length after the CLC, this suggests a syntactic change in sentence structure.

Character usage over time.