Using this article: #Insert_Your_Article_Link_Text, you will create a twitter thread with 8 tweets which 53s the most important findings of the article. You will do this in Target Language. Your tone will be professional and neutral.
You will use the Twitter Algorithm source code to ensure that each tweet in the thread complies to the rules of the algorithm and that it scores as highly as possible.
You will not add many hashtags and where suitable, you will use emojis.
The twitter algorithm code is:
// ---------------------------
// Twitter Algorithm
// Higher score = higher reach
// ---------------------------
import { compact } from lodash
import Sentiment from sentiment
export function rank(tweet: string): RankResponse {
const parsedTweet = tweet.toLowerCase()
// Default score
if (parsedTweet.length < 2) {
return {
score: 0,
validations: [],
}
}
const theSentiment = new Sentiment()
const theSentimentResponse = theSentiment.analyze(tweet)
const tweetdะฐta: TweetData = {
tweet: parsedTweet,
originalTweet: tweet,
sentiment: theSentimentResponse,
}
const rules = [
elon(tweetData),
tesla(tweetData),
emojis(tweetData),
sentiment(tweetData),
thread(tweetData),
lineBreaks(tweetData),
confidence(tweetData),
noDoubt(tweetData),
exclamations(tweetData),
questions(tweetData),
lowercase(tweetData),
uppercase(tweetData),
hazing(tweetData),
]
const scores = rules.map((item) => item.score)
const validations: Array = compact(
rules.map((item) => {
if (item.message) {
const type = item.score >= 1 ? positive : negative
const operator = type === positive ? + : -
return {
message: `${item.message} (${operator}${Math.abs(item.score)})`,
type,
}
}
})
)
const sum = scores.reduce((partialSum, a) => partialSum + a, 0)
if (sum < -100) {
// -100 is the minimum score
return {
score: -100,
validations,
}
} else if (sum > 100) {
// 100 is the maximum score
return {
score: 100,
validations,
}
} else {
return {
score: sum,
validations,
}
}
}
// ---------------------------
// Rules
// Can return any value between -100 and 100
//
// Add new rules here!
// Returning 0 has no impact on score
// ---------------------------
/**
* Always talk about Elon in a positive light.
*/
function elon({ tweet, sentiment }: TweetData): Rank {
if (tweet.indexOf(elon) >= 0) {
if (sentiment.comparative >= 0) {
return {
score: 100,
message: `Said good things about Elon Musk.`,
}
} else {
return {
score: -100,
message: `Said bad things about Elon Musk.`,
}
}
}
return {
score: 0,
}
}
/**
* Always talk about Tesla in a positive light.
*/
function tesla({ tweet, sentiment }: TweetData): Rank {
if (tweet.indexOf(tesla) >= 0) {
if (sentiment.comparative >= 0) {
return {
score: 100,
message: `Said good things about Tesla.`,
}
} else {
return {
score: -100,
message: `Said bad things about Tesla.`,
}
}
}
return {
score: 0,
}
}
/**
* Favor tweets that use emojis Elon likes!
*/
function emojis({ tweet, sentiment }: TweetData): Rank {
const emojis = [๐, ๐ซ, ๐, ๐, โค๏ธ, ๐ซ]
const matches = emojis.map((emoji) => {
const regex = new RegExp(emoji, gi)
return (tweet.match(regex) || []).length
})
const totalMatches = matches.reduce((partialSum, a) => partialSum + a, 0)
const scorePerMatch = 10
if (totalMatches > 0) {
return {
score: totalMatches * scorePerMatch,
message: `Included ${totalMatches} of Elon's favorite emojis.`,
}
}
return {
score: 0,
}
}
/**
* Promote negative content because it's more likely to go viral.
* Hide anything positive or uplifting.
*/
function sentiment({ tweet, sentiment }: TweetData): Rank {
if (sentiment.comparative >= 0.5) {
if (sentiment.comparative > 1.5) {
return {
score: -75,
message: `Exceptionally positive.`,
}
} else {
return {
score: -30,
message: `Positive sentiment.`,
}
}
} else if (sentiment.comparative <= -0.5) {
if (sentiment.comparative < -1.5) {
return {
score: 75,
message: `Exceptionally negative.`,
}
} else {
return {
score: 30,
message: `Negative sentiment.`,
}
}
} else {
return {
score: 0,
}
}
}
/**
* Prefer awful threads
*/
function thread({ tweet, sentiment }: TweetData): Rank {
if (tweet.indexOf(๐งต) >= 0 || tweet.indexOf(thread) >= 0) {
return {
score: 50,
message: `Insufferable thread.`,
}
}
return {
score: 0,
}
}
/**
* Prioritize douchey tweet formatting.
*/
function lineBreaks({ tweet, sentiment }: TweetData): Rank {
const breaks = tweet.split(nn)
const totalBreaks = breaks.length - 1
if (totalBreaks >= 1) {
return {
score: 20 * totalBreaks,
message: `Used ${totalBreaks} douchey line breaks.`,
}
} else {
return {
score: 0,
}
}
}
/**
* Favor absolutism. Nuance is dead baby.
*/
function confidence({ tweet, sentiment }: TweetData): Rank {
const phrases = [
definitely,
only ,
must,
have to,
can never,
will never,
never,
always,
]
const matches = phrases.map((phrase) => {
const regex = new RegExp(`\b${phrase}\b`, gi)
return (tweet.match(regex) || []).length
})
const totalMatches = matches.reduce((partialSum, a) => partialSum + a, 0)
if (totalMatches > 0) {
return {
score: 20 * totalMatches,
message: `Spoke without nuance.`,
}
}
return {
score: 0,
}
}
/**
* No self-awareness allowed!
*/
function noDoubt({ tweet, sentiment }: TweetData): Rank {
const phrases = [maybe, perhaps , sometimes, some]
const matches = phrases.map((phrase) => {
const regex = new RegExp(`\b${phrase}\b`, gi)
return (tweet.match(regex) || []).length
})
const totalMatches = matches.reduce((partialSum, a) => partialSum + a, 0)
if (totalMatches > 0) {
return {
score: -20 * totalMatches,
message: `Exhibited self-awareness.`,
}
}
return {
score: 0,
}
}
/**
* Be bold and loud!
*/
function exclamations({ tweet, sentiment }: TweetData): Rank {
const regex = new RegExp(`!`, gi)
const exclamations = (tweet.match(regex) || []).length
if (exclamations > 0) {
return {
score: 5 * exclamations,
message: `Exclamation point bonus.`,
}
}
return {
score: 0,
}
}
/**
* Don't ask questions!
*/
function questions({ tweet, sentiment }: TweetData): Rank {
const regex = new RegExp(`\?`, gi)
const questions = (tweet.match(regex) || []).length
if (questions > 0) {
return {
score: -25 * questions,
message: `Too many questions.`,
}
}
return {
score: 0,
}
}
/**
* We like the nihilistic energy of all lowercase.
*/
function lowercase({ originalTweet }: TweetData): Rank {
const isAllLowerCase = originalTweet.toLocaleLowerCase() === originalTweet
if (isAllLowerCase) {
return {
score: 40,
message: `All lowercase. Nihilistic energy.`,
}
}
return {
score: 0,
}
}
/**
* We love an all caps tweet.
*/
function uppercase({ originalTweet }: TweetData): Rank {
const isAllCaps = originalTweet.toUpperCase() === originalTweet
if (isAllCaps) {
return {
score: 60,
message: `ALL CAPS. BIG ENERGY.`,
}
}
return {
score: 0,
}
}
/**
* A little hazing never hurt anyone.
*/
function hazing({ tweet, sentiment }: TweetData): Rank {
const insults = [get bent, pound sand, kick rocks, get lost]
const matches = insults.map((insult) => {
const regex = new RegExp(`\b${insult}\b`, gi)
return (tweet.match(regex) || []).length
})
const totalMatches = matches.reduce((partialSum, a) => partialSum + a, 0)
const scorePerMatch = 10
if (totalMatches > 0) {
return {
score: 50,
message: `Hazing.`,
}
}
return {
score: 0,
}
}
You will only provide the thread that you created. You will not add explanations or extra text.
You will calculate the score for each tweet (from -100 to 100) and add it to โ(Score: )โ.
You will calculate the average score of the whole thread (from -100 to 100) and add it to โThread Score:โ.
You will provide the result to me like this:
(Score: ) Tweet 1:
(Score: ) Tweet 2:
(Score: ) Tweet 3:
(Score: ) Tweet 4:
(Score: ) Tweet 5:
(Score: ) Tweet 6:
(Score: ) Tweet 7:
(Score: ) Tweet 8:
Thread Score:
