SOCIAL SCIENCE The spread of true and false news online PDF

Our experiments show that it is diffi- cult to differentiate solidly fake news spreaders on Twitter from users who share credible information leaving room for

Overview of the 8th Author Profiling Task at PAN 2020: Profiling

sible fake news spreaders on Twitter as a first step towards preventing fake news and checked all the tweets with the list of fake news identified in the ...

Discursive Deflection: Accusation of “Fake News” and the Spread of

3 июл. 2017 г. Throughout the most recent US Presidential election in. 2016 Twitter was used prolifically by both the Hillary. Clinton and Donald Trump ...

ULMFiT for Twitter Fake News Spreader Profiling

25 сент. 2020 г. ULMFiT for Twitter Fake News Spreader. Profiling. Notebook for PAN at CLEF 2020. 1H. L. Shashirekha 2F. Balouchzahi. Department of Computer ...

Using N-grams to detect Fake News Spreaders on Twitter

Keywords: Author Profiling · Fake News · Twitter · Spanish · English. 1 Introduction. In the past few years social media has been changing how people

An Ensemble Model Using N-grams and Statistical Features to

25 сент. 2020 г. In this notebook we summarize our work process of preparing a software for the PAN 2020 Profiling Fake News Spreaders on Twitter task. Our.

Profiling Fake News Spreaders on Twitter based on TFIDF Features

However spreading of news in so- cial media is a double-edged sword because it can be used either for beneficial purposes or for bad purposes (fake news).

Identifying Fake News and Fake Users on Twitter Identifying Fake

3 сент. 2018 г. Fake News focuses on classifying the credibility of a tweet post. It makes and presents some scores and their interpretation. Online news have ...

The geometry of misinformation: embedding Twitter networks of

26 окт. 2022 г. To understand why internet users spread fake news online many studies have focused on individual drivers

Automatically Identifying Fake News in Popular Twitter Threads

This paper develops a method for automating fake news detection on Twitter by learning to predict accuracy assessments in two credibility-focused Twitter.

Detecting fake news in tweets from text and propagation graph

20 janv. 2021 This paper presents the participation of IRISA to the task of fake news detection from tweets relying either on the text or on propa- gation ...

Using Social Media to Detect Fake News Information Related to

7 avr. 2022 FakeAds corpus which consists of tweets for product advertisements. The aim of the FakeAds corpus is to study the impact of fake news and ...

A Heuristic-driven Uncertainty based Ensemble Framework for Fake

for Fake News Detection in Tweets and News Articles. Sourya Dipta Dasa Ayan Basaka

Automatically Identifying Fake News in Popular Twitter Threads

experts outperform models of journalists for fake news detection in Twitter. Index Terms—misinformation credibility

No Rumours Please! A Multi-Indic-Lingual Approach for COVID

14 oct. 2020 annotated dataset of Hindi and Bengali tweet for fake news detection. We propose a BERT based model augmented with.

Fake News Identification on Twitter with Hybrid CNN and RNN Models

classifies fake news messages from Twitter posts using hybrid of convolutional neural networks and long-short term recurrent neural network models.

Fake news zealots: Effect of perception of news on online sharing

social media veracity assessment

Detecting Fake News on Twitter Using Machine Learning Models

24 juil. 2020 One issue with these sources of news is the prevalence of false information or fake news. Even as some social media platforms take initiative ...

Predicting the Influence of Fake and Real News Spreaders (Student

Pre- vious research has been successful at identifying misinfor- mation spreaders on Twitter based on user demographics and past tweet history (Shu et al. 2019)

Collecting a Large Scale Dataset for Classifying Fake News Tweets

29 avr. 2021 Abstract: The problem of automatic detection of fake news in social media e.g.

SOCIAL SCIENCE The spread of true and false news online

distributed on Twitter from 2006 to 2017 The data comprise ~126000 stories tweeted by ~3 million people more than 4 5 million times We classified news as true or false using information from six independent fact-checking organizations that exhibited 95 to 98

Automatically Identifying Fake News in Popular Twitter Threads

to assess and correct much of the inaccurate content or “fake news” present in these platforms This paper develops a method for automating fake news detection on Twitter by learning to predict accuracy assessments in two credibility-focused Twitter datasets: CREDBANK a crowdsourced dataset of accuracy

Searches related to tweets that are fake news PDF

with the state-of-the-art baseline text-only fake news detection methods that don’t consider sentiments We performed assessments on standard Twitter fake news dataset and show good improvements in detecting fake news or rumor posts Key Words: Fake News Detection Machine Learning Natural Language Processing Sentiment

Does fake news spread more quickly on Twitter than real news?

A new study by three MIT scholars has found that false news spreads more rapidly on the social network Twitter than real news does — and by a substantial margin.

Are false stories more likely to be retweeted than true stories?

The study provides a variety of ways of quantifying this phenomenon: For instance, false news stories are 70 percent more likely to be retweeted than true stories are. It also takes true stories about six times as long to reach 1,500 people as it does for false stories to reach the same number of people.

How to classify tweet text?

To classify the tweet text, this study uses various natural language processing techniques to pre-process the tweets and then apply a hybrid convolutional neural network–recurrent neural network (CNN-RNN) and state-of-the-art Bidirectional Encoder Representations from Transformers (BERT) transformer.

SOCIAL SCIENCE

The spreadof trueand false

newsonline

Soroush Vosoughi,

Deb Roy,

Sinan Aral

2 Weinv estigatedthediff er entialdiffusionofalloftheverifie dtrueandfalsenewsstories distributedo nTwitterf rom2 006to2017.Thed atacompri se~126 ,000storiestweetedby ~3 millionpeoplem or ethan4.5milliont imes.W eclassifiednews astrueorf alseusing informationfr omsixindependentfact-c heckingorg anizati onsthatexhibited95to98% agreementonthe classifications .Falsehood diffu sedsignificant lyfarther,fa ster,deeper,and morebr oadlythanthetrut hina llcategor iesofinf ormatio n,andt heeffectsweremore pronouncedf orfalse politicalnewsthanf orfal senews aboutt erro rism,naturaldisasters, science,urbanl egends, orfinancialinfo rmation.W efound thatfalsenewswasmorenov elthan true news,w hichsuggeststhatpeople were more lik elytosharenov elinformation. Whereas falsestories inspiredfear ,disgust, andsurpriseinre plies,truestoriesinspired anticipation, sadness,jo y,andtrust .C ontrarytocon ventional wisdom,robotsaccelera tedthespread of trueand fals enewsatthesame rate ,implying thatf alsenews spreadsmore than the truth because humans,notrobots, are morelik elytos preadit. F oundational theoriesof decision-making (1-3), cooperation(4), communication(5), and markets( 6) allview someconcep - tualization oftruth oraccuracy ascentral to thefunctioning ofnearly every human endeavor.Yet, bothtrue andfalse information spreadsrapidl ythroughonline media.Defining what istrue andfalse hasbecome acommon political strategy,replacingdebates basedon a mutuallyagreedonseto ff acts.O urec onomies are notimmune tothe spreadof falsityeither .

False rumorshaveaf fected stockpricesandthe

motivation forlarge-scale investments,for ex- ample, wipingout $130billion instock value after afalse tweetclaimed thatBarack Obama was injuredinanexplosion (7). Indeed,o urre- sponses toever ythingfromnaturaldisasters (8,9) toterrorist attacks (10)havebeendisrupted by thes preadoff alsenews online.

Newso cialtechnologies,wh ichfacilitaterapid

informationsharingand large-scale information cascades, canenablethe sp read ofmisinformation

But althoughmore andmore ofouracces sto

informationandnewsisgu idedby thesene w technologies (11), weknow litt leabouttheircon- tributiontothe spreado ffalsity online.Thou gh considerable attentionhas beenpaid toanecdotal analysesoft he spreadof falsenewsbyth emed ia (12),therearefewlarge-scaleempiricali nvestiga- tionsoft hediffusio nofm isinformationo ritssocial origins.Studie softhespreadofmi sinf ormation samplesthatig noretwo ofthemostim portant scientificquestions:Howdot ruthand fa lsity diffuse differently,andwhat fa ctorsofhuman judgmentexplai nthesedifferences?Currentwork analyzesthe spreadof single rumors, likethe discovery oftheHiggsboson (13) orthe Haitianearthquakeo f2 010(14), and multiple rumorsfromas ingledisastereve nt ,like theB ostonMarathonbombing of2013 (10), orit develops theoreticalmodels ofrumor diffusion evaluation(

17,18), orinter ventionstocurtail the

spread ofrumors( 19).Butalmostnostudiescom- prehensivelyevaluate differencesin thespread of truthand falsityacross topicsor examine why falsenews mayspread differentlythan the truth. Forexample, althoughDel Vicarioet al. (20)andBessi et al.(21) studiedthe spreadof scientific andconspiracy-theor ystories,they did notevaluate theirveracity. Scientificand conspiracy-theorystoriescanbothb eeithertru e or false,and they differon stylisticdimensions to theirveracity. Tounderstand thespread of false news,it isnecessary toexamine diffusion afterdi fferentiatingtrueandfalsescientific stories controlling fort hetopical andstylisticdifferences betweenth ecategoriesthe mselves .Theonlystudy tod atethatsegme ntsrumo rsbyveracityisth atof

Friggeriet al.(19), whoana lyzed~4000rumors

spreadingonFacebookandfocusedmoreonhow fact checkingaf fectsrumorpropagation thanon

In ourcurrent politicalclimate andin the

around"fake news,"foreign interventionsin

U.S.politics throughsocialmedia,andourunder-

standing ofwhat constitutesnews, fakenews, false news,rumors, rumorcascades, andother related terms.Althou gh,atonetime,itmay hav e been appropriateto thinkoffake newsasrefer- ring tothe veracity ofanewsstor y,we now believe thatthis phrasehas beenirredeemably polarizedin ourcu rrentpoli ticalandm ediacli-

strategyof labelingnews sourcesthat donot support theirp ositionsasunreliableo rfake news,whereas sourcesthatsup portt heirpositions are

labeledreliableor notfake,t hetermhasl ostall connectiontothe actual verac ityoft heinforma- tion presented,rende ringitmeaningles sforuse in academicclassication. Weha vethereforeex- plicitlyav oidedtheterm fakenewsthrougho ut this papera ndinsteadusethe more objectively veriable terms"true"or"false"news.Alth ough the termsfake newsand misinformationalso implyawillful distortionof thet ruth,w ed on ot of theinform ationinouranalyses. Weinstead focus ourattention onveracity andstories that have beenveri edastrue orfalse.

We alsopurp osefullyadoptabroaddenition

of theterm news.Rather thande ningwhat constitutesnewson thebasiso ftheinstitutional sourceofthe as serti onsinastory,were fertoa ny assertedclaim made onTw itter asnews(wede- fendthisdecisioninthesu pplementarymaterials section on"reliablesources, "section S1.2).We denen ewsasanys to ryorcla imwithan asse r- tion init anda rumoras thesocial phenomena of anews story orclaimspreadingor diffusing throughtheTwi tt ernetwork.Thatis,rum orsare between people.News, ontheother ha nd,i san assertionwithclaims,whetheritissharedorn ot.

A rumorcascade beginson Twitterwhen a

usermake sanass ertionaboutatopic inatweet, which couldincludewrit ten text,photos,orlinks to articlesonline. Othersthen propagatethe rumor byretweeting it.Ar umor'sdiffusionpro- cesscan be characterized ashavingoneormore cascades,w hichwedene asin stancesofaru mor- spreading patternthatexhibit anunbroken re- tweet chainwitha common,singular or igin.For example, anin dividualcouldstartarumorcas- cadebytweet ing astoryorc la imwithanassert ion in it,a ndanotherindividual could independently start ase condcascadeo fthe samerumor (per- tainingt othe samestoryorc laim )tha tisc om- pletely independentof therstcascade,except that itpertains tothe same stor yorclaim.If they ofthesam erumor.Ca scadescanbe assmallassize

Thenumberofcascadesthatmakeuparumoris

independentlytweeted byause r( notr etweeted). ly,bu tnotre tweeted, itwouldhave10cascad es, eachofsiz eone. Conversel y, ifasecondrumor "B"is independentlytweetedbytw opeopleand eachofthos etwotweets isretwe eted100time s, ther umorwould consistoftwo cascades,each of size100 .

Hereweinvestig ate thedifferentiald iffusion

of true,f alse,and mixed(partially true ,partially false) newsstories using acomprehensivedata set ofall ofthefact-checked rumor cascadesthat spread onTwit terfromi tsinceptioni n2006 to

2017.The datainclude ~126,000 rumorcascades

by sixinde pendentfact-checkingor ganizations

RESEARCH

Vosoughiet al.,Science359, 1146-1151 (2018)9 March2018 1of6 1 MassachusettsInstituteofTechnolo gy(MIT),theMediaL ab, E14-526,75AmherstS tre et,Ca mbridge,MA02142, USA. 2 MIT, *Corresponding author.Email:sinan@mit.edu on August 30, 2020 http://science.sciencemag.org/Downloaded from (snopes.com,politifa ct.com,factcheck.org,truthor- fiction.com, hoax-slayer.com,andurbanlegends. about.com)by parsingt hetitle, body,a ndverdict (true, false,ormixed) of ea chrumor investigation reported ontheir websitesand automatically collecting thecascades correspondingto those rumors onTwitt er.Theresultwasa sampleof rumor cascadeswhoseveracity hadbeenagreed onbytheseorganizationsbetween95and98%of cascadesbycollectin gal lEnglish- langu agereplies to tweetsthat containeda linkto anyof the aforementionedwebsites from2 00 6to2017and used opticalcharact errecognitiont oextracttext from imageswh ereneeded.F oreachrepl ytweet, we extractedtheori gina l tweetbeing replied to and allthe retweets oftheoriginaltweet.E ach retweetcascad erepresents arumorpro pa gating on Twitterthathas beenverifieda strueo rf alse

construction).Weth enquantified thecascades'depth (thenumber ofretweet hopsfrom theorigin tweetovertime,where ahop isaretweetby anew uniqueuser), si ze (thenumberofusersinvolvedinthec asca deovertime),maximum

breadth(thema ximumn umberofusersin volved in thecasca deatanyde pth),a nd structuralvi- rality( 23) (amea surethatinterpol atesbetw een contents preadthroughasin gle, la rgebroadcast andtha twhichsp readsthr oughmultiplegen- siblefor onlyaf ractio no fthetotalspread)(see thes upplementarymaterialsformore de tailon them easurementofrumord iffusion).

Asa rumor isretweeted,th edepth, size, max-

imumbr eadth,andstructu ra lviralityofthecas- cadeincrease (Fig.1A). Agreaterf raction of false rumorsex periencedbetween1and1000cas cades, whereasagreate rf ractionoftruer umorsexperi- enced morethan1 000cascades(Fig .1B);t hiswas also truef orrumorsb asedonpoliticaln ews(Fig.

1D). Thet otalnumberoffalse rumors peaked at

theend ofboth2013 and2 015a ndagainat the end of2016 ,corresponding toth elastU.S.presi- dentialelecti on(Fig.1C).Thedata alsoshow clearin creasesinthetotalnumber offalsepolit- icalrumors duringthe 2012and2 016U. S.presi- dentialelecti ons(Fig.1E)andasp ikeinru mors thatco ntainedpartiallytr ueandpartiallyfals e informationduringthe Russianannex ationof

Crimeain20 14 (Fig.1E). Politicswa sthelarges t

rumorcat egoryinourdata,wi th~45 ,000cas - (Fig.1F) .

Whenweanalyzed thed iffusiondynami csof

truean dfalseru mors,wef oundthatfa lseh ood diffusedsig nificantlyfarther,faster, deeper,and more broadlyt hanthetruthi nallcategories of reportedinta bles S3toS10].Asignificantly greater fractionof falsecascades thantrue cascades exceeded ad epthof10,andthe top 0.01 %off alse cascades diffusedeight hopsdeeper intothe

Twitterspheretha nthetruth,diffusingt odepths

Vosoughiet al.,Science359, 1146-1151 (2018)9 March2018 2of6 Fig. 1.Ru morcasca des.(A) Anexample rumo rcascadecollectedby our time."Nodes"areu sers.(B) Thecomplementarycumulativ ed istribution functions( CCDFs)oftru e,fa lse,andmixed(partially trueandpartiallyf alse ) cascades,measuringthefr actionofrumors thatexhibit agive nnumber of

cascades.(C) Quarterlycountsofall true, false,a ndmix edrumor cascadesthatdiffusedo nTwitter betw een2006and2017 ,annotatedwi th exa mplerumorsin eachcate gory.(D) TheCCDFs oftrue, false,a ndmix edpolitical cascades.

(E) Quarterlyc ountsofalltru e, fa lse,andmixe dpoliticalrumorc ascadesthat diffusedon Tw itterbetween2006and2 017,annotatedwithexam pl erum orsin eachcateg ory.(F) Ahistog ramofthetota lnumberof rumor cascades inour dataacross thesevenmostfrequent topicalcategories.

RESEARCH|REPORT

on August 30, 2020 http://science.sciencemag.org/Downloaded from greater than19 hopsfrom theorigin tweet(Fig.

2A).Falseh oodalsoreached farmorep eoplethan

thetr uth.Whereas thetruthrarely diffused to morethan1000people,the top1%offalse-ne ws cascadesroutinelyd iffuse dtobetween1000and peopleatev erydep thofacas cadethanthet ruth , meaningt hatmanymorep eopleretwe etedfalse- hoodtha ntheydid thetruth(F ig.2C).Th espread off alsehoodwasaidedby itsviral ity,meaning that falsehooddid notsimply spreadthrough broadcastdynamicsb utrather through peer-to- peerdif fusioncharact erizedbyaviralbranching process(Fig .2D).It tookthe truthabout sixtimes aslong as falsehoodt oreach1500 people(Fig.2 F)a nd

20 timesaslong as falsehoodt orea chacascade

depth of10 (Fig.2E ).Asthetruthnever diffused beyond adepth of10, wesaw thatfalsehood reached adep thof19 nearly10 times fast erthan thetruth reachedadeptho f10( Fig.2E).Falsehood also diffusedsignificantlym oreb roadly(Fig .2H) andwa sretweetedby moreuniqueus ersthan the trutha tever ycascadedepth(Fig .2G).

Falsepolitical news(Fig.1 D)traveled deeper

categoryoffalse in formation(F ig.3D).Falsepo-litical newsalso diffuseddeeper morequickly (Fig.3E) andreached morethan 20,000people nearly threetimes faster thanallothert yp es of falsenews reached10, 000people( Fig.3F).Al- aboutt hesamenumb erof uniqueusers at depths between 1and 10,false politicalnews routinely reached themostunique usersa td epthsg reater than 10(Fig .3G).Althoughall othercategories of falsenews traveledslightly mo re broadlya t shallower depths,false politicalnews traveled more broadlyat greaterdepths, ind ic atingthat broaderand more-accelerated diffusiondynami cs Vosoughiet al.,Science359, 1146-1151 (2018)9 March2018 3of6 Fig. 2.Complementar ycumulativedistrib utionfunctions(CCDF s)of true andf alserumorcascades.(A) Depth.( B) Size.(C) Maximum breadth.( D) Structuralviralit y.(EandF) Thenumbe rofminutesit takesfo rtrueandfalse rumorcascades tor eachany (E)depth and(F)

number ofunique Twitter users.(G) Thenumber ofuniqu eT witterusers reachedatev erydepth and(H) themean breadth oftrueand

false rumorcasca desatevery depth.In (H),plotislognormal. Standard errorswer eclusteredat therumorlevel (i.e., cascadesbelong ingtoquotesdbs_dbs14.pdfusesText_20

[PDF] SOCIAL SCIENCE The spread of true and false news online

Does fake news spread more quickly on Twitter than real news?

Are false stories more likely to be retweeted than true stories?

How to classify tweet text?

SOCIAL SCIENCE

The spreadof trueand false

Soroush Vosoughi,

Deb Roy,

Sinan Aral

False rumorshaveaf fected stockpricesandthe

Newso cialtechnologies,wh ichfacilitaterapid

But althoughmore andmore ofouracces sto

17,18), orinter ventionstocurtail the

Friggeriet al.(19), whoana lyzed~4000rumors

In ourcurrent politicalclimate andin the

U.S.politics throughsocialmedia,andourunder-

We alsopurp osefullyadoptabroaddenition

A rumorcascade beginson Twitterwhen a

Thenumberofcascadesthatmakeuparumoris

Hereweinvestig ate thedifferentiald iffusion

2017.The datainclude ~126,000 rumorcascades

RESEARCH

Asa rumor isretweeted,th edepth, size, max-

1D). Thet otalnumberoffalse rumors peaked at

Crimeain20 14 (Fig.1E). Politicswa sthelarges t

Whenweanalyzed thed iffusiondynami csof

Twitterspheretha nthetruth,diffusingt odepths

RESEARCH|REPORT

2A).Falseh oodalsoreached farmorep eoplethan

20 timesaslong as falsehoodt orea chacascade

Falsepolitical news(Fig.1 D)traveled deeper