Detection of the Spiral of Silence Effect in Social Media

Transcription

1 Journal of Unversal Computer Scence, vol. 22, no. 3 (2016), submtted: 12/11/15, accepted: 23/1/16, appeared: 1/3/16 J.UCS Detecton of the Spral of Slence Effect n Socal Meda I-Hsen Tng (Department of Informaton Management, Natonal Unversty of Kaohsung, Tawan tng@nuk.edu.tw) Abstract: Opnon mnng has been a crucal research topc among recent studes, partcularly concernng data from socal meda. However, a wdely dscussed communcaton concern called the spral of slence effect has not been examned n opnon mnng studes. In ths paper, we propose an approach for detectng the spral of slence effect n socal meda. We beleve that the accuracy of opnon mnng can be mproved by consderng the effect of the spral of slence. The detals and steps of the detecton approach are dscussed. We also collected data from two popular socal networkng webstes, namely Facebook and Twtter, for performance measurement. Analyss fndngs show that the average accuracy of the proposed approach was hgher than 0.85, ndcatng that the approach s hghly effectve. Key Words: Spral of slence, socal network analyss, socal network, socal meda Categores: I.2.7, J.4, H Introducton Socal networkng webstes such as Facebook, Google+, and Twtter have become popular platforms for users to share deas as well as varous aspects of ther daly lves. In the area of communcaton, these types of webstes are referred to as socal meda. Socal networkng webstes are a form of worldwde socal meda n whch nformaton spreads rapdly. Furthermore, users can mmedately receve responses and feedback from other users. Therefore, people commonly use socal meda as a man channel for expressng ther opnon. For example, numerous government departments treat socal meda as an essental platform for promotng polces or poltcal agendas [Verson and Auvnen 2013]. For the aforementoned reasons, data from socal networkng webstes are consdered valuable. Effectvely analyzng and applyng these data can facltate ganng a clearer understandng of the opnons and behavors of socal meda users [Tng, Chang, and Wang 2012]. Numerous experts n the area of nformaton technology have nvestgated technques to effcently and accurately determne user opnons. Such technques nclude web mnng, text mnng, socal network analyss (SNA), sentment analyss, and opnon mnng [Agrawal, Rajagopalan, Srkant, and Xu 2003], [Somprasertsr 2010]. Among related studes, a hghly crucal theory n the area of communcaton studes has been gnored; ths theory s known as the spral of slence (SOS), and t refers users tendency to hde ther opnons when they understand that ther opnons are nonmanstream [Matthes, Morrson, and Schemer 2010]. Researchers have suggested that the effect of SOS also occurs on socal networkng webstes, whch may affect accuracy n detectng user opnons [McCurdy 2010]. Therefore, devsng a methodology that can be appled to automatcally detect the SOS effect n socal

2 Tng I.-H.: Detecton of the Spral of Slence Effect networkng webste data would be useful. Related studes on the SOS are dscussed n secton 2 of ths paper. Accordng wth the background and motvaton of ths study, we propose an approach that s based on SNA, sentment analyss, opnon mnng, and the classfcaton technque of data mnng. In ths approach, we defned a turnng pont to detect the SOS. To determne the accuracy of the proposed approach, we collected data from 2000 Facebook fan page posts, classfyng them nto two groups wth 1000 posts each, namely the SOS and nonsos groups. Subsequently, we performed SNA to determne relevant measurements, comprsng degree, closeness, dameter, betweenness, and a clusterng coeffcent. Furthermore, keywords from the posts were extracted as an addtonal parameter. These parameters were subsequently employed n assgnng the post classfcaton to determne the parameters that were useful n detectng the SOS effect. In the remander of ths paper, further background and lterature revew concernng the theory of the SOS, SOS, sentment analyss, and opnon mnng s provded. In secton 3, we propose the research desgn and the framework of the proposed approach for detecton of the SOS effect. An expermental and data analyss s presented n secton 4; we provde the dscusson and concluson n secton 5. 2 Background and Lterature Revew In ths secton, we frst ntroduce background nformaton regardng the SOS effect, whch s the theory appled n ths paper. Furthermore, related essental technques from prevous studes concernng SOS detecton are revewed, ncludng SNA, sentment analyss, and opnon mnng. 2.1 Spral of Slence The SOS (.e., the SOS effect ) s a well-known theory n the area of communcaton studes. The theory was proposed by Noelle-Neumann n 1973; Fgure 1 shows the conceptual presentaton of the SOS effect as defned by Noelle-Neumann [Noelle- Neumann 1993]. In Fgure 1, the left hand sde represents the dstrbuton of strength vewpont, whereas the rght hand sde represents publc support for people wth a mnorty vewpont. When the dstrbuton of strength vewpont becomes more prevalent, and the publc support for people wth a mnorty vewpont becomes less prevalent, people wth a mnorty vewpont start to hde ther true opnons. Eventually, they reman slent and even show support for the dstrbuton of strength vewpont. The effect represents a spral, and therefore has been called the SOS effect. In the theory, humans are gregarous anmals, and therefore requre support from one another. Humans fear lonelness; thus, people frequently revew ther vewpont to determne whether t s a unque or mnorty vewpont. If people notce that they hold a mnorty vewpont, they tend to hde t to avod lonelness and reman slent. However, some studes have suggested that slence may not be the only reacton when people fear lonelness, but there s no evdence to prove ths [Salmon and Klne 1985]

3 440 Tng I.-H.: Detecton of the Spral of Slence Effect... Fgure 1: Concept of the SOS effect [Noelle-Neumann 1993] Some researchers have clamed that because of the prevalence of anonymty n the era of the Internet and socal networkng, users can easly hde ther true denttes, and that the SOS effect s dsappearng [Web 2013]. However, many researchers have ndcated that the SOS effect remans and occurs frequently [Gearhart and Zhang 2013]. The SOS effect may be more obvous n certan stuatons than n others [Chen 2011], [C. Wang 2011], [Fu and Sm 2011], [Stratta 2004]. The bandwagon effect s another theory that s an extenson of the SOS effect. The bandwagon effect refers to a stuaton whereby people who have expressed publc opnons can clam that ther opnons are supported by a large proporton of people n order to attract other people to agree wth ther opnons. For example, canddates n an electon tend to clam that they have the support of most people and may even produce false poll results to valdate ths clam [Stratta 2004], [Fu and Sm 2011]. 2.2 Socal Network Analyss SNA s a research methodology n the area of socology for observng and analyzng the nteractons among socal actvsts to determne socal network structures [Wasserman and Faust 1994]. Because of the long hstory of SNA, numerous terms and defntons have been wdely appled n varous research areas such as socology, management, busness, bology, and nformaton technology [Wlson 1989], [June, Km, Km, and Cho 2006], [Chang, Tng, and Wang 2011], [Wang, Tng, and L 2011], [Lee, Tng, and Lou 2012], [Wang, Tng, and Wu 2013]. The basc concept and methodologes of SNA have been thoroughly explaned n two well-known books, namely Socal Network Analyss: A Hand Book and Socal Network Analyss: Methods and Applcatons [Wasserman and Faust 1994], [Scott 2000]. These works have ncluded the defntons of socal network roles and measurements of socal relatonshps (e.g., centralty, closeness, betweenness, cluster coeffcent, network dameter, and structure hole) [Wellman and Berkowtz 1988], [Burt 1992]. The followng measurements were appled n ths study:

4 Tng I.-H.: Detecton of the Spral of Slence Effect (1) Network Dameter: Network dameter s the average dstance of the shortest path for each par of nodes. (2) Densty: Densty = D/L; L s the maxmum possble lnes (.e., edges) n a graph; D s the total lnes (.e., edges) n a graph. (3) Centralty: Two dfferent types of centralty exst, namely local centralty and global centralty. Local Centralty (absolute) = Degree....Equaton (1) Local Centralty (relatve) = Degree/Degree (total) -1.Equaton (2) Global Centralty =......Equaton (3) Dstance to each nodes A B C Local absolute Centralty relatve 0.43(3/7) 0.57(4/7) 0.43(3/7) Global Centralty Table 1: Example of dfferent centralty analyses The followng three measurements are extensons of the centralty measurement: (4) Degree Centralty: Degree centralty can be employed to measure the most actve node n a network, whch s the node havng the hghest number of relatonshps wth other nodes n the network. Assumng that the hghest degree of a node s V -1, the degree centralty of the node s D(V)/( V -1) after normalzaton. (5) Closeness Centralty: In a socal network, a hgher closeness centralty ndcates a shorter average dstance between nodes. (6) Betweenness Centralty: The node that nteracts wth the hghest number of other nodes n a socal network has the hghest betweenness centralty n that network. In ths stuaton, most nodes must pass through ths node to contact other nodes. Assume that for each node, there exst two nodes, j and k, that must g communcate. jk n denotes the number of shortest paths that pass through to connect j and k. Equatons (4) and (5) measure the normalzed betweenness centralty. C C B ' B n n j k g jk n g jk CB n g 1g 2/ 2...Equaton (4)...Equaton (5) 2.3 Opnon Mnng In recent years, a hgh quantty of data has been aggregated wth the rapd growth of socal networkng webstes. Researchers from varous felds have regarded such data as valuable. However, the hgh quantty of data renders executng effcent analyss

5 442 Tng I.-H.: Detecton of the Spral of Slence Effect... dffcult, and ths s a crtcal concern n nformaton technology. Opnon mnng was developed as a soluton to ths problem, referrng to the automatzed extracton of Internet user opnons [Cho et al. 2011]. Opnon mnng, also called sentment analyss, has become a crtcal applcaton n data mnng, text mnng, and nature language processng [Hu and Lu 2004]. Dstnct from tradtonal detecton methods, opnon mnng does not focus on explct topcs and keywords, but on sentmental expressons. Therefore, the major tasks n opnon mnng are to extract sentmental messages and transform unstructured documents nto structured ones. Postve and negatve sentmental expressons can then be dstngushed and classfed by sentment analyss [Pang, Lee and Vathyanathan 2002], [Turney 2002], [Pang and Lee 2005]. Some researchers have also treated sentment as a contnuous nterval to determne the level of sentment n data [Thelwall, Buckley, Paltoglou, Ca, and Kappas 2010]. In opnon mnng, sentmental messages can be extracted from documents through two methods, namely corpus-based and thesaurus-based opnon mnng [Pang and Lee 2005]. In recent studes, the corpus-based method has provded more data than has the thesaurus-based method concernng sentmental keywords; therefore, t s regarded as the manstream method of sentment analyss. Generally, opnng mnng comprses four steps: feature extracton, opnon keyword extracton, opnon classfcaton, and opnon summarzaton (.e., determnng the connecton between opnon keywords and keyword features) [Jakob and Gurevych 2010], [Tng and Yen 2012], [Tng 2015]. Opnon mnng currently has numerous applcatons. Pang and Lee proposed an approach to classfy postve and negatve comments of moves based on opnon mnng [Jakob and Gurevych 2010]. Opnon mnng s also used for the detecton of spam or false opnons, whch s useful for users to detect true opnons on socal networks [Mukherjee et al. 2013], [Qan and Lu 2013]. Notably, opnon mnng can also be appled n the predcton of publc support for poltcal partes or publc concerns. Ths type of predcton s hghly smlar to electon polls, but focuses on data from socal meda platforms such as Twtter or Facebook [Younus et al. 2011]. In most current applcatons for opnon mnng, the SOS effect s seldom dscussed and accounted for. Ths s because t s a theory n the area of communcaton. However, as mentoned, the SOS effect has been proven to reman n socal networkng platforms [McCurdy 2010]. Therefore, developng a method of detectng the SOS effect among these platforms s valuable, and such a method can facltate mprovng the performance and accuracy of opnon mnng. Furthermore, the technque of opnon mnng s useful n modelng the SOS effect as well as n dentfyng the pattern of an SOS (e.g., the frequency of postve and negatve opnons; turnng pont). 3 Research Desgn and Framework On the bass of the background and motvaton dscussed n the prevous sectons, we developed an approach for detectng the SOS effect n socal meda. In ths study, we focused solely on data from Twtter and Facebook. Fgure 2 llustrates the research desgn and framework; the correspondng 10 steps are dscussed n ths secton as follow.

6 Tng I.-H.: Detecton of the Spral of Slence Effect Fgure 2: Research desgn and framework (1) Socal Meda Data: In ths study, Twtter and Facebook were selected as socal meda platforms. Regardng Facebook, we collected data from fan pages related to socal polcy or poltcs by usng the Facebook applcaton program nterface (API). A sample of Facebook fan page data s shown n the left panel of Fgure 3. We collected data from a fan page posts for all nteractons, comprsng lkes, shares, and comments. Fgure 3: Sample Facebook fan page data (left) and Twtter data (rght)

7 444 Tng I.-H.: Detecton of the Spral of Slence Effect... Concernng data from Twtter, we used the Twtter API to extract data durng the perod from January 2012 to March 2012; approxmately 100 mllon data tems were collected. A sample Twtter data tem s shown n the rght panel of Fgure 3. As wth Facebook data, we also collected data from Twtter posts for all nteractons, comprsng retweets, responses, and lkes. (2) Data Classfcaton: After step 1, we nvted 10 experts on SOS to determne whether an SOS effect was evdent n the dscussons. (3) Classfed Data; SOS and NonSOS: After step 2, the experts spent approxmately 2 months classfyng the data as ether data wth SOS or data wthout SOS (nonsos). Ultmately, we retaned 2000 data tems from Facebook n Chnese (1000 SOS and 1000 nonsos) and 2000 data tems from Twtter n Englsh (1000 SOS and 1000 nonsos). These data were employed n ths study for tranng and expermentaton. (4) Socal Network Analyss: All the data collected n the prevous steps were appled to a relatonshp matrx to represent the nteractons among the users n a post, wth factors ncludng lkes, comments, subcomments, and shares. Fgure 4 shows an example matrx transformaton; the relatonshps were dentfed and the frequences of nteracton were talled. For example, f user A lkes user B s comment, a value of 1 s appled to the matrx; no weghtng was performed for dfferent nteractons. The fnal matrx was then used for SNA measurements. Fgure 4: Example relatonshp matrx transformaton In ths study, the followng SNA measurements were employed: network dameter (ND), average degree (AD), closeness(c), betweenness (B), cluster

8 Tng I.-H.: Detecton of the Spral of Slence Effect coeffcent (CC) and densty (DE). These measurements were determned for each post collected. Equaton 6 represents the vector of SNA. DMSNA ND, AD, C, B, CC, DE...Equaton (6) (5) Opnon Mnng: Opnon mnng was the man step n ths study, after whch the sentmental value of the keyword n a post was analyzed. The process of opnon mnng s shown n Fgure 3. In the process of opnon mnng, dscusson posts were frst subjected to sentence segmentaton. Through the use of a thesaurus, features (.e., keywords) n a sentence could be selected. The features were subsequently analyzed to measure the degree of sentmental value by usng a sentment dctonary and WordNet. Sentmental value was the essental measurement n ths study to detect the effect of the SOS, whch was analyzed n the fnal step of the experment. Dscusson Posts Synonyms Dctonary Sentment Dctonary Wordnet Englsh/Chnes e Sentence Segmentaton Feature Selecton Degree of Sentmental Measurement Analyss of the Change of Sentmental Measurement Fgure 3: Process of opnon mnng After the measurement of sentmental value, for each dscusson data tem, we employed a vector, as shown n Equaton (7). In Equaton (7), a s the sentmental vector of dscusson. O n denotes the opnon degree of the n th response n dscusson. DMOMa O O...,, 0 1 O n...equaton (7) For each dscusson, there s another vector DMOMb, or turnng pont vector, as shown n Equaton (8). Ths vector comprses AO and TP, where AO denotes the average opnon degree of dscusson and TP denotes the turnng pont of dscusson. DMOMb AO, TP...Equaton (8)

9 446 Tng I.-H.: Detecton of the Spral of Slence Effect... mn TP M...Equaton (9) Equaton (9) represents the turnng pont measurement. In ths equaton, M s the total responses n a dscusson, and m n denotes the n th response. Therefore, the value of TP s greater than 0 and less than 1. The turnng pont m n s the pont when a postve sentmental measurement decreases and remans below zero, or when a negatve sentmental measurement ncreases and remans above zero. (6) Vector Classfcaton: Three generated vectors, namely DMSNA, DMOMa, and DMOMb, were classfed and used for tranng to understand the crucal parameters. We used the Wakato Envronment for Knowledge Analyss (Weka) and C4.5 for the classfcaton. (7) Parameter Determnaton through Informaton Gan: After the processng n the prevous step, we used the Weka to determne the mportance of the parameters of the three vectors by applyng nformaton gan. Through ths analyss, we could determne whch parameters were sgnfcant for classfyng post data nto the SOS effect or nonsos effect groups. (8) Inducton: Inducton s another method of manually determnng parameter mportance; n ths study, ths method was employed as a supplement to the prevous step. (9) Crucal Parameter Value Determnaton: After steps 7, 8, and 9, we expected to possess the values of crucal parameters that could be appled n dstngushng SOS from nonsos data. For example, TP n Equaton (9) represents the turnng pont; by ths step, we obtaned the optmal value of TP for detectng the SOS effect. (10) Approach for Automatc SOS Detecton: Fnally, we developed an approach to automatcally detect the SOS effect on the bass of the results acqured n the prevous steps. We generated a set of rules and values for the detecton, ncludng crucal parameters and values. 4 Experment and Evaluaton We organzed a seres of experments accordng to the approach developed n the prevous steps, employng the crucal parameters and values acqured, to test the performance of our method n detectng the SOS effect. We collected data from Facebook and Twtter, as mentoned n steps 1 and 2, by employng the research desgn and framework ntroduced n secton 3. After classfyng the data, we retaned a total of 4000 data tems, consstng of 2000 data tems from Facebook and another 2000 from Twtter. The data from Facebook manly employed tradtonal Chnese (a common wrtten language n Tawan and Hong

10 Tng I.-H.: Detecton of the Spral of Slence Effect Kong). Among the 2000 data tems from Facebook, 1000 were SOS tems, whereas the other 1000 were nonsos tems. Concernng the data from Twtter, the language employed was Englsh. As wth the data from Facebook, we obtaned 1000 SOS data tems and 1000 nonsos tems. A summary of the collected data s shown n Table 2. Facebook (Tradtonal Chnese) Twtter (Englsh) Total Wth SOS Wthout SOS Total Table 2: Summary of the collected data To determne the accuracy of our proposed approach n detectng the SOS effect, we analyzed all the data tems and calculated the related measurements [see Tab. 3]. Weka was agan employed; the evaluaton method appled a 10-fold valdaton. The accuracy, precson, and recall were measured for data from both Facebook and Twtter. The analyss results generally ndcated that our approach acheved a hgh accuracy n detectng the SOS n the data from Twtter (Englsh), and a low accuracy for data from Facebook (tradtonal Chnese). However, the accuracy for both data was adequate: the accuracy was or hgher for each platform, wth an average accuracy between both platforms of 0.878; ndcatng that more than 80% of the total data wth SOS could be detected accurately. We nvestgated the reason for the relatvely hgh detecton accuracy for Englsh SOS data, compared wth that for tradtonal Chnese. On observng the data, we found that most of the errors occurred when users employed ambguous words. Notably, ths occurred mostly for users employng tradtonal Chnese. One reason ths may have occurred s because there s a hgher number of ambguous terms n tradtonal Chnese than n Englsh. Therefore, the employed dctonary may need to be expanded to more adequately dstngush the terms. Another reason may have been the dfferent cultures of Chnese- and Englsh-speakng users; ths s a notable topc concernng cultural dfferences that may be employed as a research focus n the future. Accuracy Precson Recall Englsh SOS (Twtter) Tradtonal Chnese SOS (Facebook) Average Table 3: Analyss results (10-fold valdaton)

11 448 Tng I.-H.: Detecton of the Spral of Slence Effect... In addton to the average accuracy for both data sources, we tested the method accuracy for dfferent numbers of posts n a thread (every 100 posts from posts). The test results are shown n Fgure 4 and Fgure 5; n both fgures, the x axs represents the number of posts and the y axs denotes the accuracy of SOS detecton. As shown n Fgure 4, the accuracy for most cases of Facebook data was between 0.85 and 0.95, whch s consdered hghly stable. Ths mples that there was no sgnfcant dfference n accuracy when the number of Facebook posts vared. Accuracy 0,95 1 0,9 0,85 0,8 0,75 0,7 0,65 0,6 0,55 0, Number of posts (Facebook n Tradtonal Chnese) Fgure 4: Accuracy for Facebook data wth dfferent numbers of posts As shown n Fgure 5, the accuracy for most cases of Twtter data was between 0.8 and The accuracy was slghtly hgher only when the number of posts was As evdenced n Fgure 5, the performance was hghly stable, ndcatng that there was no sgnfcant dfference n accuracy when number of Twtter posts vared. Accuracy 0,95 0,9 0,85 0,8 0,75 0,7 0,65 0,6 0,55 0, Number of posts (Twtter n Englsh) Fgure 5: Accuracy for Twtter data wth dfferent numbers of posts

12 Tng I.-H.: Detecton of the Spral of Slence Effect Conclusons and Future Studes Opnon mnng s a hghly popular and crucal research method, partcularly n the era of socal networkng: Opnon mnng can be appled to determne the opnons of socal meda users. Recently, several studes have focused on opnon mnng; however, the crtcal topc of the SOS effect has rarely been appled n such studes because t may cause bas when analyzng socal meda data. In ths study, we developed an approach to detect the SOS effect. The approach combnes the technques of opnon mnng (.e., sentment analyss) and SNA; we beleve that the pattern of the SOS may be modeled on the sentmental value of the nteractons among socal meda users. After employng the approach proposed heren, we determned crucal parameters by observng the mean accuracy of the nformaton obtaned from the classfcaton technque and nducton. These parameters can be used for the detecton of SOS. We collected 2000 data tems each from Facebook and Twtter for tranng the parameters and for use n a seres of experments to measure the performance of the proposed SOS detecton approach. The analyss fndngs show that the data accuracy values for Facebook and Twtter were and 0.843, respectvely, wth an average data accuracy of between the platforms. Therefore, the accuracy of the proposed approach s hgher than expected. Ths s our frst paper concernng the applcaton of SNA and opnon mnng technques to detect the SOS effect. On the bass of our fndngs, several future research topcs can be nvestgated. For example, we would lke to use ths approach to verfy electon results n Tawan and the Unted States, as well as to compare the dfferences among electon polls. We would also lke to compare dfferences among cultures and countres, as well as to expand our research to other socal meda platforms. Acknowledgments Ths work was funded by the Mnstry of Scence and Technology, Tawan. The project ttle s Applyng Socal Network Mnng to Dscover the Effect of Spral of Slence Detecton and Dscovery Technque: A Comparatve Emprcal Study of Dfferent Platforms and Cultures ; the project number s H MY3. We express our apprecaton to the three anonymous revewers for ther valuable feedback and comments n mprovng the qualty of ths paper. References [Agrawal, Rajagopalan, Srkant and Xu 2003] Agrawal, R., Rajagopalan, S., Srkant, R., Xu, Y.: Mnng Newsgroups Usng Networks Arsng From Socal Behavor ; In World Wde Web 2003 Conference. Budapest, Hungary (2003), pp [Burt 1992] Burt, S. P.: Structural Holes ; Cambrdge, MA, USA: Harvard Unversty Press (1992). [Chang, Tng and Wang 2011] Chang, P.-S., Tng, I.-H., Wang, S.-L.: Towards Socal Recommendaton System Based on the Data from Mcroblogs ; 2011 Internatonal Conference

13 450 Tng I.-H.: Detecton of the Spral of Slence Effect... on Advances n Socal Networks Analyss and Mnng (2011), pp [Chen 2011] Chen, K.: A Test of the Spral of Slence Theory on Young Adults Use of Socal Networkng Stes for Poltcal Purposes ; Iowa State Unversty (2011). [Cho et al. 2011] Cho, K. S., Yoon, J. Y., Km, I. J., Lm, J. Y., Km, S. K., Km, U.-M.: Mnng Informaton of Anonymous User on a Socal Network Servce ; 2011 Internatonal Conference on Advances n Socal Networks Analyss and Mnng (2011), pp [Fu and Sm 2011] Fu, W. W., Sm, C. C.: Aggregate Bandwagon Effect on Onlne Vdeos Vewershp: Value Uncertanty, Popularty Cues, and Heurstcs ; Journal of the Amercan Socety for Informaton Scence and Technology, Vol. 62, No. 12 (2011), pp [Gearhart and Zhang 2013] Gearhart, S., Zhang, W.: Gay Bullyng and Onlne Opnon Expresson: Testng Spral of Slence n the Socal Meda Envronment ; Socal Scence Computer Revew, Vol. 32, No. 1 (2013), pp [Hu and Lu 2004] Hu, M., Lu, B.: Mnng and Summarzng Customer Revews ; In KDD Seattle, Washngton, USA (2004), pp [Jakob and Gurevych 2010] Jakob, N., Gurevych, I.: Usng Anaphora Resoluton to Improve Opnon Target Identfcaton n Move Revews ; In Proceedngs of the 2010 ACL Conference. Uppsala, Sweden (2010), pp [June, Km, Km and Cho 2006] June, T., Km, J., Km, B. J., Cho, M. Y.: Consumer Referral n A Small World Network ; Socal Networks, Vol. 28, No. 3 (2006), pp [Lee, Tng and Lou 2012] Lee, T.-L., Tng, I.-H., Lou, M.-M.: A study on product dffuson of socal network stes - takes HTC as an example ; In The 2012 Internatonal Conference on Internet Studes. Bangkok, Thaland (2012). [Matthes, Morrson and Schemer 2010] Matthes, J., Morrson, K. R., Schemer, C.: A Spral of Slence for Some: Atttude Certanty and the Expresson of Poltcal Mnorty Opnons ; Communcaton Research, Vol. 37, No. 6 (2010), pp [McCurdy 2010] McCurdy, P.: Breakng the Spral of Slence Unpackng the Meda Debate wthn Global Justce Movements: A Case Study of Dssent! and the 2005 Gleneagles G8 Summt ; Interface, Vol. 2, No. 2 (2010), pp [Mukherjee et al. 2013] Mukherjee, A., Kumar, A., Lu, B., Wang, J., Hsu, M., Castellanos, M., Ghosh, R.: Spottng Opnon Spammers usng Behavoral Footprnts ; In Proceedngs of SIGKDD Internatonal Conference on Knowledge Dscovery and Data Mnng. Chcago, USA (2013). [Noelle-Neumann 1993] Noelle-Neumann, E.: The Spral of Slence: Publc Opnon-Our Socal Skn ; (2nd ed.). Chcago: The Unversty of Chcago Press (1993). [Pang and Lee 2005] Pang, B., Lee, L.: Seeng stars: explotng class relatonshps for sentment categorzaton wth respect to ratng scales ; In Proceedngs of the 43rd Annual Meetng on Assocaton for Computatonal Lngustcs. Stroudsburg, PA, USA (2005), pp [Pang, Lee and Vathyanathan 2002] Pang, B., Lee, L., Vathyanathan, S.: Thumbs up?: sentment classfcaton usng machne learnng technques ; In Proceedngs of the ACL-02 conference on Emprcal methods n natural language processng - Volume 10. Stroudsburg, PA, USA (2002), pp

14 Tng I.-H.: Detecton of the Spral of Slence Effect [Qan and Lu 2013] Qan, T., Lu, B.: Identfyng Multple Userds of the Same Author ; In Proceedngs of Conference on Emprcal Methods n Natural Language Processng (EMNLP- 2013). Seattle, USA (2013). [Salmon and Klne 1985] Salmon, C. T., Klne, F. G.: The Spral of Slence Ten Years Later. An Examnaton and Evaluaton ; In K. R. Sanders, L. L. Kad & D. Nmmo (Eds.), Poltcal Communcaton Yearbook Carbondale, Edwardsvlle (1985), pp [Scott 2000] Scott, J.: Socal Network Analyss: A Handbook ; London: SAGE Publcaton (2000). [Somprasertsr 2010] Somprasertsr, G.: Mnng Feature-Opnon n Onlne Customer Revews for Opnon Summarzaton ; Computer, Vol. 16, No. 6 (2010), pp [Stratta 2004] Stratta, R. J.: Expanded Crtera Donors n Kdney Transplantaton: A Treadmll or Bandwagon Effect? ; Medscape Transplantaton, Vol. 5, No. 2 (2004). Retreved from [Thelwall, Buckley, Paltoglou, Ca and Kappas 2010] Thelwall, M., Buckley, K., Paltoglou, G., Ca, D., Kappas, A.: Sentment strength detecton n short nformal text ; Journal of the Amercan Socety for Informaton Scence and Technology, Vol. 61, No. 12 (2010), pp [Tng 2015] Tng, I.: Towardng The Effect of Spral of Slence Detecton Based on Socal Network Analyss and Opnon Mnng Technques ; In CIST 15: Internatonal Conference on Computer and Informaton Scence and Technology. Ottawa (2015), pp [Tng and Yen 2012] Tng, I., Yen, C.: Opnon Groups Identfcaton n Blogsphere based on the Technques of Web and Socal Networks Analyss ; In The 4th Internatonal Conference on Machne Learnng and Computng. Hong Kong, Chna (2012), pp [Tng, Chang and Wang 2012] Tng, I.-H., Chang, P. S., Wang, S.-L.: Understandng Mcroblog Users for Socal Recommendaton Based on Socal Networks Analyss ; Journal of Unversal Computer Scence, Vol. 18, No. 4 (2012), pp [Turney 2002] Turney, P. D.: Thumbs up or thumbs down?: semantc orentaton appled to unsupervsed classfcaton of revews ; In Proceedngs of the 40th Annual Meetng on Assocaton for Computatonal Lngustcs. Stroudsburg, PA, USA (2002), pp [Verson and Auvnen 2013] Verson, O., Auvnen, A.: Socal Meda-The New Power Of Poltcal Influence ; Suomen Tov-Thnk Tank (2013). [Wang 2011] Wang, C.: The Emergence of Spral of Slence from Indvdual Behavors: Agent-based Modelng of Spral of Slence ; In The 64th Annual Conference of the World Assocaton for Publc Opnon Research (WAPOR). Amsterdam, The Netherlands (2011). [Wang, Tng and L 2011] Wang, C.-S., Tng, I.-H., L, Y.-C.: Tawan Academc Network Dscusson va Socal Networks Analyss Perspectve ; 2011 Internatonal Conference on Advances n Socal Networks Analyss and Mnng (2011), pp [Wang, Tng and Wu 2013] Wang, K.-Y., Tng, I.-H., Wu, H.-J.: Dscoverng nterest groups for marketng n vrtual communtes: An ntegrated approach ; Journal of Busness Research, Vol. 66, No. 9 (2013), pp [Wasserman and Faust 1994] Wasserman, B., Faust, K.: Socal Network Analyss : Methods and Applcatons ; New York: Cambrdge Unversty Press (1994).

15 452 Tng I.-H.: Detecton of the Spral of Slence Effect... [Web 2013] Web, T.: Socal Meda and Endng The Spral Slence ; (2013). Retreved from [Wellman and Berkowtz 1988] Wellman, B., Berkowtz, S. D.: Socal Structures: A Network Approach ; Cambrdge, England, UK: Cambrdge Unversty Press (1988). [Wlson 1989] Wlson, D. S.: Levels of Selecton: An Alternatve to Indvdualsm n Bology and the Human Scence ; Socal Networks, Vol. 11, No. 3 (1989), pp [Younus et al. 2011] Younus, A., Quresh, M. A., Asar, F. F., Azam, M., Saeed, M., Touheed, N.: What Do the Average Twtterers Say: A Twtter Model for Publc Opnon Analyss n the Face of Major Poltcal Events ; 2011 Internatonal Conference on Advances n Socal Networks Analyss and Mnng (2011), pp