Can Blog Communication Dynamics be correlated with Stock Market Activity? Munmun De Choudhury Hari Sundaram Ajita John Dorée Duncan Seligmann

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1 Can Blog Communiaion Dynamis be orrelaed wih Sok Marke Aiviy? Munmun De Choudhury Hari Sundaram Ajia John Dorée Dunan Seligmann Ars Media & Engineering Arizona Sae Universiy Collaboraive Appliaions Researh Avaya Labs ABSTRACT In his paper we develop a simple model o sudy and analyze ommuniaion dynamis in he blogosphere and use hese dynamis o deermine ineresing orrelaions wih sok marke movemen. This work an drive argeed adverising on he web as well as failiae undersanding ommuniy evoluion in he blogosphere. We desribe he ommuniaion dynamis by several simple onexual properies of ommuniaion e.g. he number of poss he number of ommens he lengh and response ime of ommens srengh of ommens and he differen informaion roles ha an be aquired by people (early responders / lae railers loyals / ouliers). We sudy a ehnology-savvy ommuniy alled Engadge (hp:// There are wo key onribuions in his paper: (a) we idenify informaion roles and he onexual properies for four ehnology ompanies and (b) we model hem as a regression problem in a Suppor Veor Mahine framework and rain he model wih sok movemens of he ompanies. I is ineresingly observed ha he ommuniaion aiviy on he blogosphere has onsiderable orrelaions wih sok marke movemen. These orrelaion measures are furher ross-validaed agains wo baseline mehods. Our resuls are promising yielding abou 78% auray in prediing he magniude of movemen and 87% for he direion of movemen. Keywords Blogosphere informaion roles ommuniaion dynamis Suppor Veor Regression Sok marke movemen.. INTRODUCTION In his paper we develop a simple model o sudy and analyze ommuniaion dynamis in he blogosphere and use hese dynamis o mine ineresing orrelaions wih sok marke movemen. The problem is imporan beause i provides insighs ino undersanding ommuniaion paerns of people; for example how onex affes hese paerns; how he informaion roles of people affe ommuniaion emporal and opial dynamis of informaion roles e. The ommuniaion dynamis furher seem o yield orrelaions wih erain exernal evens as well jusifying heir prediive power. Ofen hese dynamis are useful o orporae organizaions who are ineresed in idenifying he moods of people on exernal ommuniies in response o produ releases and ompany relaed evens. The dynamis an also drive argeed adverising on he web as well as enable undersanding ommuniy evoluion in he blogosphere. There has been prior work on modeling ommuniaion dynamis [578] and heir orrelaion wih exernal evens [46]. The auhors in [4] deermine orrelaions beween aiviy in Inerne message boards (hrough frequeny ouns of relevan messages) and sok volailiy and rading volume. In [6] he auhors aemp o deermine if blog daa exhibi any reognizable paern prior o spikes in he ranking of he sales of books on Amazon.om. They presen hand-rafed prediaes o show ha orrelaion measures indiae visible blog menions ahead of any evidene in sales rank. There has also been some work on idenifying imporan bloggers based on ommuniaion aiviy in [9]. The auhors aemp o deermine ho onversaions in he blogosphere hrough agiaors and summarizers by esablishing disriminans. In [0] he auhors idenify opinion leaders who are responsible for disseminaing imporan informaion o he blog nework using a variaion of he PageRank algorihm. However modeling informaion roles and ommuniaion dynamis in he prior work have been done in a onex-independen manner. The main onribuion of his paper is a simple onexual framework o model he ommuniaion dynamis among people and undersand how hey an be orrelaed wih evens exernal o he blogosphere. In his work we have speifially looked a he impa on soks of ehnology ompanies due posings in a gadge-disussing blog; however he framework an be exended easily o oher senarios. We define sok movemen of a ompany as he normalized differene beween he reurns on wo onseuive days. We assume ha he sok movemen on a erain weekday an be orrelaed wih he ommuniaion dynamis in he pas week. This is reasonable beause blog ommuniaion is ofen found o preede he ourrene of a real world even [6]. Hene we haraerize he ommuniaion dynamis in a blog hrough several onexual feaures for a pariular ompany. These onexual feaures are: he number of poss he number of ommens he lengh and response ime of ommens srengh of ommens and he differen informaion roles ha an be aquired by people (early responders / lae railers loyals / ouliers). We use hese feaures and sok marke movemen of he ompany over N weeks for raining an SVM regressor. The rained parameers are used o predi he movemen a he (N+) h week. These resuls are validaed using wo baseline mehods: firsly by omparing wih a non-onex aware ase and seondly using a linear ombinaion of he onexual feaures. Our ehnique supersedes boh wih error of 22 % and 3% in prediing he magniude and direion of movemen respeively. The res of he paper is organized as follows. In seions 2 and 3 we presen a ompuaional model for informaion roles and deermining onexual feaures for sok movemen prediion. Seion 4 disusses he mehod of deermining orrelaion beween ommuniaion and sok movemens. In seion 5 we disuss some experimenal resuls followed by onlusions and fuure worn seion 6.

2 2. MODELING INFORMATION ROLES In his seion we desribe a se of informaion roles ha an be assumed by people involved in ommuniaion in he blogosphere. Roles of people affe heir ommuniaion and are herefore a par of he soial onex. We aegorize posers of blog ommens ino he following informaion roles: early responders / lae railers orresponding o heir response ime; and loyals / ouliers orresponding o he measure of ommuniaion aiviy (frequeny oun of poss or ommens auhored). 2. Roles due o response behavior In his seion we desribe he informaion roles of people wih respe o he response imes of heir ommens (on blog poss). Inuiively people who are regularly involved in ommuniaion in he blogosphere develop erain sruures whih haraerizes heir response behavior or when hey would wrie ommens on a erain pos. Analysis of suh paerns an define he informaion role of he person over a long period of ime. To deermine roles due o response ime we define a normalized response ime frequeny disribuion for eah poser in he following manner. Normalized Response Time: Response ime of a ommen is defined as he ime (in seonds) elapsed beween he publishing of he original blog and he publishing of he ommen. We define he normalized response ime of a ommen suh ha i depends on (a) he ime a whih i was published and (b) he rank of he ommen defined as a meri ha depends on is relaive posiion among he se of all ommens. If a ommen is posed very soon afer he blog pos is rans onsidered high and vie versa. Our moivaion for his definiion of normalized response ime follows from Figure. We onsider he wo ommens shown in green dos. We noie ha he 38 h ommen (firs green do) has a shor response ime while he 43 rd ommen (seond green do) has a very long response ime. However we also noie ha he differene in heir ranks is very low. Hene he wo ommens have been posed o he blog in a omparaively shor span of ime. The effeive normalized response ime hus inorporaes he rank meri in order o urb he skewness due o response imes. Response Time Respons e Dis ribuion of a Blog Pos Commen IDs Figure : Skewness in normalizing response ime. We define he normalized response ime as follows. Le m be he ime a whih a ommen was posed by a person Alie o a blog pos p x. Le us furher assume ha s and e are respeively he publishing ime of he pos and he las ommen. Also le be he rank of Alie s ommen. The response ime r (x) of pos p x is defined as ( m s ) ( ) κ < > r ( x) = θ + θ2 e s n ( x) where and 2 are wo hosen weighs and n (x) is he number of ommens on pos p x. The equaion suggess ha he normalized response ime of he ommen is minimized when Alie has responded early and her ommen has low rank. Early Responders / Lae Trailers: We now define wo aegories of behavior: early responders and lae railers. Early Responders are people who respond o messages or blog poss quikly. Lae Trailers are people who ah up wih an on-going disussion owards he end of ommuniaion on he opi. If he mean response ime r over all ommens in a period of ime is less han a hreshold hen he behavior in ha ime period is aken o be Early Responder. If i is greaer han hen he behavior is defined as a Lae Trailer. 2.2 Roles due o measure of aiviy In his seion we desribe wo differen informaion roles of people wih respe o heir overall pas ommuniaion aiviy. They are: loyals and ouliers. People who are noied o auhor large numbers of ommens or poss on a erain opi an be onsidered as loyals o ha opi; while ouliers are all he people who are no haraerized by any sruure in heir ommuniaion aiviy. For example hey are he people who appear o ommen on blog poss sporadially. Assume ha a person has wrien a oal number of C ommens on all poss in a large ime period (say 50 weeks) abou a erain ompany. We onsru an aiviy disribuion (Figure 2) for all suh people. In order o deermine he informaion role of a pariular person using his disribuion we define a suiable hreshold over he maximum number of ommens in he disribuion. If C is greaer han hen he person s role would be a loyal while i will be an oulier if C is less han or equal o. For example in he figure Charles is a loyal while Brian is an oulier wih suiably hosen. # Commens oshean Charles 2imer Aiviy Disribuion ADMINV Clarion CW Brian Bhaal Bozo Figure 2: Aiviy disribuion. Bains Anoine Brendan 3. CONTEXTUAL MODELING OF COMMUNICATION DYNAMICS In his seion we develop several onexual feaures whih haraerize he ommuniaion dynamis of people. For he se of all feaures we assume ha ha he sok movemen y of a ompany on a erain weekday depends on he ommuniaion aiviy in he pas week (abou he same ompany) in he ime

3 period ( 6) o. These feaures are hen used o predi sok movemen and deermine is orrelaion wih ommuniaion aiviy a a fuure ime. We now inrodue some feaures of our daase on he Engadge blog. The blog is haraerized by wo modes of ommuniaion: poss and ommens wrien in response o poss. Every ommen on a erain pos is also marked for is signifiane by oher users. There are five levels: highes ranked highly ranked neural low ranked and lowes ranked. I migh be noed ha hese srengh levels are differen from he rank of a ommen desribed in seion 2.. The level assoiaed wih a ommen a any insan of ime represens he omposie signifiane indiaed by all users. Le us now disuss he feaures. Number of poss: The higher he number of poss abou a erain ompany he more impa ha pariular day has on a fuure even. Hene he firs onexual feaure is he number of poss per day in he pas week n p -6 n p -5 n p. Le a day ( i) where 0 i 6 p p 2 p ki be he poss on a pariular ompany. Number of ommens: The higher he number of ommens a erain ompany he more impa ha pariular day has on a fuure even. Hene he seond onexual feaure is he number of ommens in all he poss per day in he pas week n -6 n -5 n. n = n ( x) < 2 > i x= i where n -i (x) is he number of ommens on pos p x x and 0 i 6. Lengh and Normalized response imes of ommens: The mean and sandard deviaion of he lengh and response imes (normalized beween 0 and ) of ommens per day migh also affe he sok movemens. For example high mean and high sandard deviaion migh reveal ha he ineres in he poss ha day was high bu was peaky or fluuaing. While high mean and low sandard deviaion would reveal ha he ineress were high bu were mosly fla or onsisen among he posers. Le a day ( i) l -i (x) be he average lengh of ommens on pos p x where x and 0 i 6. Therefore we define our hird onexual feaure as he uple (µ l -i l -i) l µ i = l i ki x= σ ( x) l i = l i( x) l i k i x= 2 < 3 > Again le a day ( i) r -i (x) be he average normalized response ime of ommens on pos p x where x and 0 i 6. Therefore we define our fourh onexual feaure as he uple (µ r - i r -i) where he mean and he sandard deviaion an be ompued similar o equaion < 3 >. Srengh of ommens: The signifiane of ommens on eah day of he wees a useful indiaor of impa of he ommens on exernal evens. If several people have highly ranked ommens on a erain day hose ommens are likely o impa he sok movemens of he orresponding ompany more han less ranked ommens. Le R -i be he number of ommens ha are highes ranked in all he poss p p 2 p ki a day ( i) where 0 i 6. Similarly we an define he oher four ses as R 2 -i R 3 -i R 4 -i R 5 -i respeively. Then he 5 h feaure is he 5-uple (R -i R 2 -i R 3 -i R 4 -i R 5 -i ) orresponding o eah day ( i) for all he seven days in he week. Size of he Early Responder / Lae Trailer se: This feaures akes ino aoun he habiual behavior of he people involved in ommuniaion on a erain day ( i). The sizes of hese informaion roles on a pariular day are useful beause i onains useful informaion. If he size of he early responders se is large i means ha he orresponding poss are likely newer and so he impa on sok movemen migh be high; while if he size of lae railer se is large i migh refle an older pos whose impa migh have already happened or less likely o happen in he fuure. The 6 h feaure is herefore he uple (E L) where E is he se of all people on day ( i) who are early responders while L is he se of all lae railers on day ( i). Size of he Loyals / Ouliers se: We onjeure ha he impa of a pariular pos on he sok movemen of a ompany in he fuure also depends on who is posing ommens o ha pos (based on exen of ommuniaion aiviy in he pas). We use he se sizes of he informaion roles: loyals and ouliers ha we disussed in he previous seion. Le us onsider he aiviy disribuion for all he poss p p 2 p ki a day ( i) where 0 i 6. Le S L -i and S O -i be respeively he se of loyals and ouliers a day ( i). { : ( ) } L S = i x C x > θ { : ( ) } O S = i x C x < θ < 4 > where C(x) is he oal number of ommens wrien by poser x a day ( i) where 0 i 6 and is a suiably hosen hreshold. Le us furher assume ha S L and S O be he se of loyals and ouliers over he whole raining period. There are several ineresing impliaions of hese ses. If he ardinaliy of he se S L -i S L is large i means ha mos of he posers who are oherwise loyal have responded o poss on day ( i). I migh indiae regulariy in ommuniaion aiviy whih migh furher mean ha he poss on ha day migh have low impa on exernal evens. On he oher hand if he ardinaliy of he se S O -i [S L U S O ] is large i indiaes several ouliers posing ommens. Suh large aenion fous from exernal users migh imply he preourrene of a big even. Hene he 8 h feaure is he uple ( S L -i S L S O -i [S L U S O ] ) orresponding o eah day ( i). 4. DETERMINING CORRELATION In his seion we presen a Suppor Veor regression framework o predi he sok movemens for a ompany whih would reveal he exen of orrelaion wih he ommuniaion dynamis. Firs of all we disuss he mehod of ompuing sok marke movemen. In order o deermine heir orrelaion wih ommuniaion dynamis i is imporan o ake ino aoun he effe of he overall sok marke senimen as well. For example a negaive movemen of he sok reurns of a pariular ompany may be aribued due o negaive movemens in he overall sok marke index (e.g. NASDAQ ISE S&P500 e). We define he sok movemen of a ompany a a day o be he hange in sok reurn from he losing value of he pas day normalized by he reurn of he pas day. Closing value for a

4 ompany is he value of sok whih exiss a he end of he aouning period (one day). The movemen is deermined as follows y = ( ϕ ϕ ) ϕ where is he sok reurn of he ompany a day. Similarly we deermine he overall marke movemen as ( ) y η = where is he sok reurn of he NASDAQ index a day (NASDAQ beause we are fousing on ehnology ompanies). Hene he ne sok movemen for he ompany is y = y y η. < 5 > Now we presen an SVM regression framework o predi sok movemen. Le us represen he ommuniaion daa (omprising he onexual feaure veors) as x = 2 N where N is he number of weeks over he pas for a erain ompany. Also le us assume he sok movemens daa be y = 2 N for he orresponding N weeks for he same ompany. SVM regression funion f(x) is rained on {(x y ) (x N y N )}. I is esed on he inremenal sample (x N+ y N+ ) o ge he predied movemen y N+. The error in prediion is ompued as E= (y N+ - y N+ )/ y N+. 5. EXPERIMENTAL RESULTS In his seion we presen he experimenal resuls. Firs we presen wo baseline frameworks and hen desribe he naure of he Engadge daase. This is followed by experimenal resuls. 5. Baseline Mehods Commen Frequeny: The firs baseline mehod for deermining orrelaion of sok marke movemens uses he frequeny of ommens per day. We assume again ha he sok movemen y on a erain weekday depends on he number of ommens in he pas week in he ime period ( 6) -. We furher use a linear regressor o learn he orrelaion oeffiiens inremenally based on sok movemens and number of ommens. Linear Relaionship among feaures: In his mehod we assume a linear relaionship beween he onexual feaures and use a linear regressor o learn he orrelaion oeffiiens inremenally. The regression fi is given as Y = X where is he veor of 2 n he orrelaion oeffiiens and Y and X are he veors of movemens and ommuniaion feaures. Hene for a given raining se of n weeks he regressor predis he sok movemen on a pariular weekday a he (n+ ) h week (using he orrelaion oeffiiens learn during raining). 5.2 Resuls of SVM Regressor In his seion we disuss our daase and he resuls of prediion using SVR. Our daase omprises he following daa iems Google geing ino e-books (Jan 22) Apple Beales seles rademark lawsui (Feb 5) Google oubids Mirosof for Dell bundling deal (May 25) B B 2 SVR Aual B B 2 SVR Aual B B 2 SVR Aual B B 2 SVR Aual Mirosof buying Yahoo rumor (May 4) Nokia E6i E65 (Jun 25) Nokia realls 46M baeries (Aug 4) iphone release (Jun 29) The FTC o ondu anirus review of Google and DoubleClik deal (May 29) Open Tex Corporaion exends alliane wih Mirosof (Aug 20) Nokia NGage gaming servie (Aug 29) Ipod lassi ipod ouh (Sep 5) US STOCKS- Teh drag rips Dow Nasdaq exends drop (Nov 29) APPLE GOOGLE MICROSOFT NOKIA Mirosof To Aquire Parlano (Aug 30) Mirosof loses EU ani-rus appeal (Sep 7) Mirosof Xbox 360 Arade release (O 23) Figure 3: Visualizaion of sok movemens wih ime on verial sale. B and B 2 are he wo baseline ehniques disussed in seion 5.. Blue bubbles indiae posiive movemen and red bubbles negaive movemens. Sizes of he bubbles represen magniude of movemen. The SVR prediion is found o follow he movemen rend very losely wih an error of 3.4 %.

5 rawled from he Engadge [] sie: a se of blog poss orresponding ommens lengh and srengh measures as well as who posed hem and a wha ime. These daa were olleed for four differen ompanies: Apple Mirosof Google and Nokia o apure diversiy of paerns. There were a oal number of 2469 blog poss 4372 ommens and 862 users in he daase in a ime period saring January 2007 o November The sok marke reurns for he ompanies (as per he NASDAQ index) were olleed from Google Finane [2]. Now we presen he resuls of he experimens (Figure 3) performed using he wo baseline mehods and he SVM regression ehnique. We ompare hem agains aual sok movemen for he four ompanies. In Figure 3 he predied and aual movemens have been shown aross ime on a verial sale wih blue bubbles indiaing posiive movemen and red bubbles negaive movemens. The orrelaions beween he ommuniaion aiviy and sok movemen have been shown hrough several represenaive evens. Eah of hese evens has been olleed from he New York Times [3] websie. For ease of reading and onsrains of spae he movemens in he figure have been hosen o be represenaive days in whih hanges are signifian. They span over a span of 50 days and are hosen using suiable hresholds for eah ompany. Hene he same row in Figure 3 migh no imply he same day aross ompanies. However he error has been predied over all monhs of raining daa. The resuls of he experimens are revealing. We observe ha he wo baseline mehods are no able o adequaely apure he subleies in variaion of sok movemens. I is only afer he ourrene of a big even ha he baseline mehods ry o ompensae for i by showing a large movemen laer. However he SVR is able o apure he fluuaions muh beer. This is beause erain disussions on Engadge ofen our regarding fuure evens. The SVR ehnique being able o apure a wide array of onexual feaures and also being able o learn heir relaionships dynamially follows he aual sok movemen beer. I is herefore observed ha in majoriy of he ases he SVR mehod is loses o he magniude of aual movemen (error: 22%) ompared o he wo baseline mehods (error in s baseline mehod: 48%; in 2 nd baseline mehod: 33%). I is ineresing o noe ha he SVR does a beer job in following he movemen rend (orrespondene in olor of bubbles) wih an error of 3.4% (error in s baseline mehod: 36%; in 2 nd baseline mehod: 29%). This migh be aribued o he fa ha our onex aware model an predi he direion of movemen very well; bu he magniude of movemen is ofen affeed by unpreedened faors e.g. how he even affes oher ompanies ompany saemens e whih migh no have raes in he pas. I migh also be noed here ha prediion of sok marke movemen is an exremely hallenging problem whih no only depends upon he disussions in a ommuniy (e.g. messages exhanged in forums blogs e) bu are affeed by several unforeseen faors whih migh no be apured in disussions a an earlier poin of ime. For example he evens Google oubids Mirosof for Dell bundling deal (May 25) and Open Tex Corporaion exends alliane wih Mirosof (Aug 20) are no found o be presen in Engadge disussions in he week long aiviy used for prediion; his explains he disrepany and higher error in predied movemen for ha day. We also emphasize ha his wors aemped o mine ineresing orrelaions of blog ommuniaion wih sok marke aiviy. The exisene of any ausal relaionship beween he wo remains an open quesion and is beyond he sope of his paper. 6. CONCLUSIONS In his paper we have developed a simple model o sudy and analyze ommuniaion dynamis in blogosphere and use hose dynamis o deermine ineresing orrelaions wih sok marke movemen. We haraerized he ommuniaion dynamis in a blog hrough several onexual feaures for a pariular ompany. These onexual feaures were: he number of poss he number of ommens he lengh of ommens response ime of ommens srengh of ommens and he differen informaion roles ha an be aquired by people (early responders / lae railers loyals / ouliers). We used hese feaures and sok marke movemen of he ompany over N weeks for raining an SVM regressor. We predied he sok movemen using an inremenal sample a N+. Our ehnique supersedes wo baseline mehods wih a mean prediion error of 22 % for magniude and 3.4% for prediing he direion of movemen. There are several ineresing direions o fuure work. We would like o improve our analysis of he informaion roles o idenify people wih variable onsequenes of heir ommuniaion aiviy. The onexual model an also be refined by inorporaing lusering of ags of ompanies haraerizing people by idenifying response regions of heir ommens e. I migh also be ineresing o see if here is an implii maro propery ha underlies ommuniaion dynamis on he blogosphere and if ha maro propery is aouned for by a voal minoriy or majoriy. 7. REFERENCES [] Engadge hp:// [2] Google Finane hp://finane.google.om/finane. [3] New York Times hp:// [4] W. ANTWEILER and M. Z. FRANK (2004). Is All Tha Talk Jus Noise? The Informaion Conen of Inerne Sok Message Boards. Journal of Finane 59(3 ): [5] M. D. CHOUDHURY H. SUNDARAM A. JOHN e al. (2007). Conexual Prediion of Communiaion Flow in Soial Neworks Proeedings of he IEEE / ACM / WIC Inernaional Conferene on Web Inelligene (WI '07). San Jose CA [6] D. GRUHL R. GUHA R. KUMAR e al. (2005). The prediive power of online haer Proeeding of he elevenh ACM SIGKDD inernaional onferene on Knowledge disovery in daa mining Chiago Illinois USA [7] R. KUMAR J. NOVAK P. RAGHAVAN e al. (2003). On he bursy evoluion of blogspae. Proeedings of he 2h inernaional onferene on World Wide Web. Budapes Hungary ACM: [8] B. 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