Most people nowadays tend to follow such accounts because they know that these users are popular and posting engaging and relevant content. However, getting verified is not easy. So do your things to impress Twitter that you are also deserving of that check.
Also read: why are smartphones getting bigger? Though there is nothing wrong about tweeting that only contains text, pictures will entice people to take a look at your post and even visit your profile. Also read: Why Microsoft saved Apple? Growing your following on Twitter does not happen by any accident. Though you can buy Twitter followers, you will still need to exert some effort to sustain a huge following. We are a close-knit community of Tech lovers, and we aim to bring out unique and useful content for our users for the sake of both benefit and entertainment.
We like to keep our ears and eyes logged into the Techno-domain and keep up with the Latest updates. Home How To. October 2, Reading Time: 5 mins read. Contents 1 Twitter Character Limit 1. Tags: Followers Twitter. Next Post. If you need a Twitter word counter you can use the counter on our homepage to get the word count. But again, the character count is what Twitter uses not the Twitter word count. After you type your text in the textbox, you can see an additional 4 metrics.
They are the Twitter bio character limit, the Twitter DM character limit, the Twitter profile name character limit and the Twitter username character limit. All these limits will show up in red once the limit has been exceeded. Below the textbox there are three buttons. The "clear text" button which will delete all text from the textbox, the "copy text" button which will copy all text inside the textbox and the "Post Tweet" button. The "Post Tweet" button will post whatever is inside the textbox onto Twitter provided you are already signed in to your Twitter account.
The first metric is the Twitter DM character limit. The DM limit is 10, characters which is fairly long considering that it uses 10 times as many characters as Instagram does for their DMs. Using our Characters To Words conversion tool , that would factor out to be an estimated to words. To contact someone through a DM simply click on their profile and click the envelope button. The next metric is the Twitter bio length limit.
For this we are limited to characters. This is where you can put a few details about who you are. After that the next metric is your username. The username character limit is 15 characters.
This is also what Twitter uses as your URL. For example once you choose your username you can go to twitter. The last metric on our Twitter counter is the profile name and it is limited to 50 characters.
This is typically your first and last name or can be a nickname or really just any name that identifies you. One difference between the profile name and username is that the username cannot be changed but the profile name can changed anytime you like.
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. The reference line indicates the CLC.
Moving averages for the number of characters a , words b , and sentences c , including standard errors. The moving averages show an increase in tweet length post-CLC. Character, word, and sentence usage display a similar increase post-CLC. Moving averages for the number of characters per word a , characters per sentence b , and words per sentence c , including standard errors. Word length increased temporarily post-CLC but then decreased to the previous level. Sentences contained more characters and words post-CLC.
Figure 5 shows a large amount of pre-CLC tweets In comparison, a much smaller proportion of post-CLC tweets 1. In other words, doubling the character limit appears to have decreased the hindrance by a factor of ten. Character-usage distribution; pre and post-CLC. This density distribution shows a large proportion of pre-CLC tweets within the upper range of — characters, whereas the proportion of post-CLC tweets within the upper range of — characters was reduced by a factor of ten.
Figure 6 shows the distribution of word usage in tweets pre and post-CLC. Again, it is shown that with the characters limit, a group of users were constrained. This group was forced to use about 15 to 25 words, indicated by the relative increase of pre-CLC tweets around 20 words. Interestingly, the distribution of the number of words in post-CLC tweets is more right skewed and displays a gradually decreasing distribution.
In contrast, the post-CLC character usage in Fig. Word-usage distribution; pre and post-CLC. This density distribution shows that in pre-CLC tweets there were relatively more tweets within the range of 15—25 words, whereas post-CLC tweets shows a gradually decreasing distribution and double the maximum word usage. To test our first hypothesis, which states that the CLC reduced the use of textisms or other character-saving strategies in tweets, we performed token and bigram analyses.
Firstly, the tweet texts were separated into tokens i. The total number of tokens in the pre-CLC tweets is 10,, including , unique tokens. The total number of tokens in the post-CLC tweets is 12,, which comprises , unique tokens. For each unique token three T -scores were computed, which indicates to what extent the relative frequency was affected by Baseline-split I, Baseline-split II and the CLC, respectively see Fig.
Figure 7 presents the distribution of the T -scores after removal of low frequency tokens, which shows the CLC had an independent effect on the language usage as compared to the baseline variance. That is, more variance in token usage as compared to Baseline-split I, but less variance in token usage as compared to the CLC. Therefore, Baseline-split II i. In other words, a gradual change in the language usage as more users became familiar with the new limit.
The T -score indicates the variance in word usage; that is, the further away from zero, the greater the variance in word usage. To minimize natural-event-related confounds the T -score range, indicated by the reference lines in Fig. Furthermore, we removed tokens that showed greater variance for Baseline-split I as compared to the CLC.
Tables 4 — 7 present a subset of tokens and bigrams of which occurrences were the most affected by the CLC. The tokens that occurred relatively less frequently post-CLC are presented in Table 4. These tokens comprise: symbols e. In summary, the words that occurred relatively more frequently pre-CLC represent mainly informal language use, such as contractions, unconventional spellings, symbols and profanity. Table 5 presents tokens that occurred relatively more frequently post- CLC, these tokens comprise: articles i.
Overall, the tokens that occurred relatively more frequently post-CLC represent more formal language usage as compared to the pre-CLC tokens in Table 4. Table 6 presents bigrams that occurred relatively more frequently pre-CLC. Again, the results suggest that there was relatively more informal language usage, that is, relatively more frequent occurrences of self-referential language, which implies a more personal and subjective language usage.
Importantly, the introduction of extra prepositions can also explain the increase in sentence length after the CLC. The second hypothesis about a potential increase in the use of adjectives, adverbs, articles, conjunctions, and prepositions, was tested using a POS analysis. Table 8 displays the relative frequencies of POS categories. Particularly, the CLC induced an increase in the usage of articles, conjunctives, and prepositions as compared to other POS categories.
This increase means that the CLC changed the syntactic structures of tweets, which is also supported by the finding that sentence length increased. Unexpectedly, the relative frequency of adverbs and adjectives did not increase after the CLC. In addition, the difference between Baseline-split I and Baseline-split II shows more variation between week 3 and week 4 as compared to week 1 and week 2.
This suggests a trend in the language usage initiated by the CLC. This bar chart shows the effect of the CLC on the part-of-speech structure of sentences as compared to Baseline-split I and Baseline-split II for time-frame specifications see Fig.
The CLC induced an increase in the relative usage of articles, conjunctions, and prepositions. The relative usage of interjections decreased more than other categories and shows the highest baseline variance due to the relatively low frequency. We investigated the effect of the character limit change CLC on the language usage in tweets.
The results indicate that the CLC has, in fact, affected the language usage in tweets. The first hypothesis was supported; the pre-CLC tweets comprise relatively more textisms, such as shortenings, contractions, unconventional spellings, symbols and numerals. The second hypothesis was partially supported. As expected, the grammatical structure was affected by the CLC: post-CLC sentences are longer and comprise more articles, conjunctives, and prepositions than pre-CLC sentences.
However, adjectives and adverbs did not increase in relative frequency. To discuss the results and implications, this section is structured as follows: first, we discuss an important insight about the results, that is, a change in the formality of language usage.
After this, each of the investigated POS components are discussed separately. We conclude with possible interpretations of the results with regard to user behavior and limitations of our study. The CLC seems to have brought about a qualitative change in language usage in tweets. Pre-CLC tweets contain relatively more informal language i. This change in formality is specifically evident in the relative frequencies of the personal pronoun ik I and the article word de the , which decreased and increased, respectively.
Previous n-gram research has shown that the frequencies for ik and de are indicators of informal and formal language usage Bouma, Particularly, ik is used very frequently in self-referential and subjective texts such as personal social-media messages. On the other hand, de is used relatively more frequently in neutral and objective texts such as news articles and books.
The results suggest that the CLC has led to a general change in the formality of language usage on Twitter. Articles indicate whether a noun refers to a specific entity or to an unspecified entity or class of entities e.
This information is not always essential, hence, articles can be excluded to save space or reduce the number of words, a strategy that characterizes both telegraphese and textese, Carrington, ; Oosterhof and Rawoens, Articles occurred relatively more frequently after the CLC.
With sufficient space, apparently, users prefer to include articles. Conjunctions are used to link words, phrases, or clauses. The increase in conjunctions after the CLC may have multiple causes. Secondly, more available space also means there is more room for summations and subordinate clauses, thus, increasing the need for conjunctions. Another explanation for the increase in conjunctions is the pre-CLC usage of conjunctive symbols instead of words e.
Prepositions can describe the spatial arrangement of entities e. However, they are also routinely extended to depict the relations between abstract ideas, such as intentions and contrasts e.
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