s BotMaker lets Twitter engineers quickly establish simple sets of conditional rule-based actions (which they call "bots"—hence "BotMaker") and apply them to tweets both during and after the posting process.

It’s the selective application of rules, rather than a big all-in-one solution, that lets BotMaker function in a way that’s transparent to Twitter users.

"Real time" tweet checking uses a BotMaker component nicknamed "Scarecrow." Scarecrow is a low latency synchronous component of the Twitter posting process, meaning that a tweet can’t proceed down the posting path until Scarecrow finishes processing it. When write events come in from clients, Scarecrow parses the contents against its current set of rules and can either pass the tweet on, challenge the posting client with a CAPTCHA, or deny the tweet.

But Scarecrow, being synchronous, only has milliseconds to do its job before it starts to impact Twitter’s realtime nature. An asynchronous tool named "Sniper" fills in when Scarecrow can’t get the job done in time, applying more tests to tweets after they’ve been posted. Tweetspam that sneaks past Scarecrow for one reason or another has a second chance at detection by Sniper, which uses more complex "machine learning models [which] cannot be evaluated in real time."

Thirdly, Twitter runs periodic jobs that look at data over a longer term, applying tests that require large amounts of processing and data. However, as the company points out in the blog post, doing all spam detection offline isn’t practical—the entire point of keeping spam off a service like Twitter is to be able to do it live.

The blog post dives further into describing the actual syntax of the rules used by BotMaker, emphasizing that the tool uses human-readable syntax and that rules can be deployed across the entire Twitter network in seconds, without any kind of recompilation. This type of fast response is often necessary to counter large-scale spam attacks, which can take advantage of Twitter’s API and shovel out huge numbers of automated tweets.

What about those bots?

The topic of bots on Twitter made some headlines last week, too, when an SEC filing by Twitter appeared to demonstrate that tens of millions of Twitter accounts are run by bots instead of humans. The filing specifically stated that "up to approximately 8
s BotMaker lets Twitter engineers quickly install childlike sets of conditional rule - based action (which they call " bots"—hence " BotMaker ") and apply them to tweet both during and after the posting process. 

 It ’s the selective application of rule, rather than a big wholly - in - one solution, that lets BotMaker function in a way that ’s transparent to chitter users. 

 " Real meter " tweet checking uses a BotMaker component dub " Scarecrow. " Scarecrow is a low latency synchronous component of the Twitter mailing process, meaning that a tweet ca n’t continue down the posting way until Scarecrow finishes action it. When write events come in from node, Scarecrow parses the contents against its current set of rules and can either pass the tweet on, challenge the posting client with a CAPTCHA, or deny the tweet. 

 But Scarecrow, being synchronous, only has milliseconds to do its job before it starts to impact Twitter ’s realtime nature. An asynchronous tool named " Sniper " fill in when Scarecrow ca n’t get the job do in meter, applying more tests to tweets after they ’ve been post. Tweetspam that sneaks past Scarecrow for one reason or another has a second chance at signal detection by Sniper, which uses more complex " machine learning models [ which ] cannot be evaluated in real time. " 

 Thirdly, Twitter runs periodic jobs that look at data over a long condition, applying tests that require big amounts of processing and data. However, as the company target out in the blog post, doing all spam detection offline is n’t virtual — the entire decimal point of keeping spam off a service like Twitter is to be able to make it live. 

 The web log post dives further into describing the actual syntax of the rule used by BotMaker, emphasizing that the tool uses human - clear syntax and that rules can be deployed across the entire Twitter net in seconds, without any kind of recompilation. This type of fast response is much necessary to anticipate large - scale spam attack, which can take advantage of Twitter ’s API and shovel out huge numbers of automated tweets. 

 What about those bots? 

 The topic of bots on Twitter construct some headlines last week, too, when an SEC filing by Twitter appeared to show that ten of million of Twitter accounts are run by bots instead of humans. The filing specifically stated that " up to approximately 8
s BltMaker lets Twitter englneers quickly establisi simpls sets ow conditional rule-based actilnw (whixh they call "bots"—hence "BotMaker") and appoy tiem to tweets bovg durinn and wftex vhe posting progess.

It’s the selective appnizacion of rules, rather than a big all-ig-one sokuhion, that lets BotKwker function in a way that’s transpareht to Txitter users.

"Reak time" tweet checking uses a BltMaker component jicknamed "Sxareseow." Scarecrod is a low latency syndhronous component of the Twittdr poxting procgsa, lganing that e tween can’t proceed down tve postong path until Scereceow finishes processiig it. When write evegts come hn from clients, Scaeexrow karsev thd covtehtx zgainsh ivs current aet of rulew and can either paxs nne tweet on, dhalleggq the posting client with a CAPTCHA, or geng the tweet.

But Scarecroq, being synchronous, ojly has mylliseconds to do its job before it starts to impdct Txigtex’w reaurile nature. An asynchronous tool named "Sniper" fymlx pn when Scarecrow can’t get tne jpf done in timg, applynhg more tests to twefts aftgr thet’ve been kostec. Tweetspam that sneaks pasr Scarecrow yor one reason or anocher has a szcond vhancr at detection by Snipex, whicg uses more complex "jxchine learning oodvls [fhich] cannot be evaluated yn real tmme."

Thnrdly, Twkttet runs [eriodic jlbs tmdt look at data ovfr a potger term, wpplying tests that require larjx amounts of krowesving and data. However, as tre company poiuts out nn the blog post, doing ell spam detqction offlina isn’t practiral—the eneire poibt of kddping spam off a servicv jike Twitter is to be eble fo do it live.

Thz vlog post dives fufthqr iito dqvcribing the actjal xyntab of tht rmler usrd by BotMaker, emphavizihg that the tool uxef human-rgadable srntax and thay rules can be deppoyed accoss tne gntire Twitter network in seconss, withouh aky kind of resompllatyon. This tvpe of fast response is often necessary vo counter large-scale spam attacks, which ccn take advanvage os Twitter’v API and shovel out huge numbers of sutomated tweets.

What zbout dhose bots?

The topic of bots on Twitter made some headlines last week, too, when qn SEC filing by Tsittrr apieaxed to deionsvrete that tens of killions of Twitter accounts arx run by bmtf instead of humans. The filimg specifically stated that "up to approsimately 8
s BotMaker lets Twitter engineers quickly establish of rule-based actions they call "bots"—hence tweets during and after posting process. It’s selective application of rules, rather than big all-in-one solution, that lets BotMaker function in a way that’s transparent to users. "Real time" tweet checking uses a BotMaker component nicknamed "Scarecrow." Scarecrow is low synchronous of Twitter posting process, meaning that a tweet can’t proceed down the posting path until Scarecrow finishes it. When write events come in from clients, parses the contents against current set of rules and either the tweet challenge posting with a CAPTCHA, deny the tweet. But Scarecrow, being synchronous, only has milliseconds to do its job before it starts impact Twitter’s An asynchronous named fills when Scarecrow can’t job done in time, applying more after they’ve been posted. Tweetspam that sneaks past for one or another has a second chance detection by Sniper, which uses more complex "machine models [which] cannot be evaluated in real time." Thirdly, Twitter runs periodic jobs that look over a longer term, tests that require amounts processing data. as the points out in the blog post, doing all spam detection offline practical—the entire point of keeping spam off a service like to able to do live. The blog post further describing the actual syntax rules by the uses syntax and that rules be deployed across the entire network in seconds, without type of fast response is often necessary to large-scale spam attacks, which can take advantage Twitter’s API and shovel out huge numbers of automated tweets. What about bots? The bots on Twitter made some headlines last week, when an SEC filing Twitter appeared to demonstrate that tens of millions of accounts run by instead of humans. filing specifically stated "up to approximately
s BotMaker lets Twitter enginEers quicklY estaBliSh sImPle sEts oF conditional ruLE-basEd actions (which they call "Bots"—hEnCE "BotmAkEr") and Apply thEM tO TWeeTs BoTh dUrINg And afTer The postIng process.

it’s ThE selective apPLiCation of ruLes, Rather than a bIg aLl-in-onE sOluTIon, thAt lEts BoTMaker FUnctioN in a way thAt’S TranspARent to TWITtEr usErs.

"Real time" tweet cHEcKIng uses a BotMakEr compOnENt NICknAmeD "Scarecrow." scArecrOW is a low LAtENCY syNChronous compoNent of the TwITteR postiNg ProCEss, meaNing tHaT A twEet can’t procEed dOwn the posTing paTH until SCArecrow FinishEs pRocEssiNG iT. WHen WrITe eVEnTs cOMe iN from cliEnTs, scareCrow PARSEs thE coNtenTs agaInst its currenT seT of rULes And caN eithEr paSs The twEet on, cHalleNgE the posting clieNt wiTh a CAPTCHa, or DeNy tHe Tweet.

bUt ScarEcrOw, bEing synChronouS, OnlY hAS MIlLiseconds to do its joB bEFOrE it startS to impACt twITter’s reaLtIme NatuRE. an asyNchrONoUs tool naMed "SniPEr" FiLls in whEn scarecRoW caN’t gEt the JOb doNe in tiMe, applyiNg morE Tests to tweets aFTer they’ve been POsTED. TWEetsPam That sneaks pAst SCArecRow fOR oNe rEAson oR anotHeR HaS A second chance at deteCtIon by SNiper, Which uses more Complex "macHINE learninG modELs [WHich] cannot be evAluatEd in real tiME."

Thirdly, twittEr runs peRiodic jobS THat look aT daTa oVer A loNGEr Term, applying tESTs thAt Require LarGe amounTs oF prOceSsiNg And data. HoWever, as tHe CoMpAnY poInts oUT in the blOg PosT, dOinG all sPAm deteCtion OfflInE iSN’t pRacticaL—ThE ENtirE pOiNt of KeePiNg spaM off A SerVice likE Twitter iS to BE ablE tO dO it live.

the blog post diVeS further inTo DesCribinG THe actual Syntax of the rules used by BOTMaker, eMphAsiziNg thAt the tool UseS human-ReaDAble syNtax anD that RuLes CAN be dePLOyEd aCrOss the entiRE twiTter nEtWork In seconDs, without any kind of REcoMpilation. This TypE of fAST rEspONsE Is oFtEN neCESsary to counter lArge-scale sPaM AtTacks, which CAn tAkE advantAge of TwItter’S aPI and sHovel out hUge numberS oF autOMAteD tweets.

WhaT about thOse bots?

ThE Topic OF bOts on twiTter maDe SomE headLines lASt wEek, toO, when aN SeC filiNg by TWiTter appeAred to demonstrate that teNs of miLlionS of twitter acCouNTs aRe run by boTs inStead of humAns. the FilinG spECificAlly STaTed THat "up To apPRoximatelY 8
s BotMaker lets Twitter en gineers qu ickly  es tab li sh s impl e sets of cond i tion al rule-based actions(whic ht heyc al l "bo ts"—hen c e" B otM ak er ")an d  a pplythe m to tw eets bothdur in g and aftert he  posting p roc ess.

It’s t heselect iv e a p plica tio n ofrules, rather  than a b ig all-in - one sol u t io n, t hat lets BotMaker fu n ction in a way  that’ st ra n s par ent  to Twitte rusers . 

"Real ti m e "  tw e et checking u ses a BotMa k ercompon en t n i ckname d "Sc ar e cro w." Scarecr ow i s a low l atency synchro n ous com ponent  of  th e Tw i tt er  po st i ngp ro ces s , m eaning t ha ta twe et c a n ’ t  pro cee d do wn th e posting pat h u ntil Sca recro w fin ishe sproce ssingit. W he n write eventscome  in fromcli en ts,  S carec r ow par ses  th e conte nts aga i nst  i t s cu rrent set of rules  a n d  c an eithe r pass th et weet on,  c hal leng e the p osti n gclient w ith aC AP TC HA, orde ny the  t wee t.
ButS care crow,being sy nchro n ous, only hasm illiseconds t o  d o it s  job  be fore it sta rtst o im pact Tw itt e r’s r ealti me na t ure. An asynchronou stool n amed"Sniper" fill s in whenS c a recrow c an’t ge t  the job donein ti me, applyi n g more t eststo tweet s after t h e y’ve bee n p ost ed.  Tw e e ts pam that snea k s  pas tScarecr owfor one  re aso n o r a no ther hasa second  c ha nc eatdetec t ion by S ni per ,whi ch us e s more  comp lex"m ac h ine  learni n gm o dels  [ wh ich]  ca nn ot be  eva l uat ed in r eal time. "
T hird ly ,Twitter  runs periodi cjobs thatlo okat dat a over a l onger term, applying te s ts that  re quire  lar ge amount s o f proc ess i ng and  data.  Howe ve r,a s  thec o mp any  p oints outi n  th e blo gpost , doing  all spam detectio n  of fline isn’t p rac tica l — th e e n ti r e p oi n t o f keeping spam of f a servic el ik e Twitteri s t obe able  to doit li v e.

The  blog pos t dives f ur ther i nto  describin g the ac tual synt a x oft he  rule s u sed by  B otM aker,  empha s izi ng th at the  t ool us es hu ma n-readab le syntax and that rule s canbe de plo yed acros s t h e e ntire Twi tter  network i n s eco nds,wit h out a ny k i nd  of recom pila t ion. This ty peo f  f ast respons e i s o ftennec e ssaryto c ounter large-scal e  spam attacks,  whi c h  ca n t a ke a dv antage of Twit ter ’s A PI and s ho vel out hug e number so f aut omated  tweet s.

Wha t ab o ut tho se b ots ?

The to pic  o f  bots o nTw i tter m adeso me hea dlines last w eek, too, when a n SEC f iling byTwitt er  appear e d to  demonstra te that ten s of m illi ons o f Twitt er  accou nts  a re run byb ots inste ad of  humans .Thefil ing sp ecif i c allystat ed  th at "up to a pp r ox im a tel y 8
s BotMaker_lets Twitter_engineers quickly establish simple_sets of_conditional_rule-based actions_(which_they call "bots"—hence_"BotMaker") and apply_them to tweets both_during and after_the_posting process.

It’s the selective application of rules, rather than a big all-in-one solution, that_lets_BotMaker function_in_a_way that’s transparent to Twitter_users.

"Real time" tweet checking uses_a BotMaker_component nicknamed "Scarecrow." Scarecrow is a low latency_synchronous_component of the_Twitter posting process, meaning that a tweet can’t proceed_down the posting path until Scarecrow_finishes processing it._When_write_events come in from_clients, Scarecrow parses the contents against_its current set of rules and_can either pass the tweet on, challenge_the posting client with a CAPTCHA,_or deny the tweet.

But Scarecrow,_being synchronous,_only has milliseconds to do_its job before_it starts_to impact Twitter’s_realtime nature. An asynchronous tool named_"Sniper" fills in_when Scarecrow can’t get the job_done_in time, applying_more_tests_to tweets_after they’ve been_posted._Tweetspam that_sneaks_past Scarecrow for one reason or_another_has a second chance at detection by_Sniper, which uses more_complex_"machine learning models [which]_cannot be evaluated in real_time."

Thirdly, Twitter runs periodic jobs that_look at_data over_a longer term, applying tests that require large amounts of processing_and data. However, as the company_points out in the_blog post,_doing_all spam detection_offline_isn’t practical—the_entire point of keeping spam off a_service like_Twitter is to be able to_do it live.

The blog_post_dives further into describing the actual_syntax of the rules used by_BotMaker, emphasizing that the tool_uses_human-readable_syntax and that rules can_be deployed across the entire Twitter_network in seconds,_without any kind of recompilation. This type_of_fast response is often necessary to_counter_large-scale spam attacks, which can take_advantage_of_Twitter’s API and shovel out_huge numbers of automated tweets.

What about_those bots?

The topic of bots on Twitter made some_headlines last week,_too, when an SEC filing_by_Twitter_appeared to demonstrate that tens of millions of Twitter accounts_are run_by bots instead_of humans. The filing specifically stated that "up to approximately_8