Detecting and Visualizing the Dispute Structure of the Replying ...

2010 International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery

Detecting and Visualizing the Dispute Structure of the Replying Comments in the Internet Forum Sites Yun-Jung Lee Center for U-Port IT Research and Education Pusan National University Busan, Republic of Korea [email protected]

Jung-Min Shim, Hwan-Gue Cho, Gyun Woo* Dept. of Computer Science and Engineering Pusan National University Busan, Republic of Korea {kildin, hgcho, woogyun}@pusan.ac.kr

Compared with plain web pages, one of the popular features of blogs and the web discussion boards is the facility for enabling the interactive communication among users. Most of the current Internet users are usually talking one another using personal blogs provided by Internet portal sites. The most basic form of blog interactions is a comment, a reader-contributed reply to a specific article posted in the blog. Blog owners can interact with the readers who are interested in their articles through the comments. Furthermore, most bloggers admitted that the feedback through comments is an important motivation for posting articles [2], [3]. The role of comments becomes more important in cyber communities, especially for the forum style blogs or Internet discussion boards where many users writing comments on. Though a commenter typically writes one comment for an article, it is not unusual that a commenter writes several comments to the same post. If a commenter disputes with the author or other commenters, he/she repeatedly writes some comments to insist his/her own opinion or to show contradiction other’s opinion. We may find new information which does not appear in the post by reading such disputing comments belong to the post. So, a commenter writing several comments to the same post is likely to dispute with other commenters. According to the survey of ‘naver,’ a popular portal site in Korea, only about 0.06% of ‘naver’ users make more than 25% comments of total amount comments of ‘naver’ [4]. Also, it suggested that the amount of the comments written by only about 750 users, corresponding 10% of total commenters in ‘naver,’ is about 50% of total comments, and most comments written by such bursty commenters tend to be useless comments such as spam or duplicated comments with same contents. These useless comments may distract the sound discussion threads and disturb other users in cyber communities. Unfortunately, since the display method adopted by is most of the current sites the linear sequence of comments according to their temporal order, it is very hard to trace and review the older comments; the only way allowed is to

Abstract—Comparing with the existing web pages, one of the popular features of blogs and the web discussion boards is the capability of the interactive communication among users. In online communities such as weblogs or Internet discussion boards, users can read articles, as well as write some comments to the articles to express his/her opinion. These kinds of replying comments become an important means of communication between the author who writes the article and the readers of it. Sometimes, we can find new information that does not appear in the contents of the article by reading comments posted to the article. Also, we can figure out various opinions in comments by reading controversial comments. Popular articles, however, frequently get up to thousands of comments, which is too much to be read in a reasonable time. Especially, to find dispute relations in the comments, we have no alternative but to read all the comments. Although there have been several studies to extract an opinion or a controversy from comments or social networks, most of them tend to be dependent on the language used or the typing errors of the contents. In this reason, we propose a method for extracting the dispute relations from comments and visualizing them including the involved commenters. Since comments written by disputing commenters tend to appear in turns, we consider only the order of commenters to detect pairs of commenters in disputing. So, our method is not affected by the language used nor typos in comments. Also, the dispute relations are visualized by an undirected graph, and it is helpful to grasp the degree of controversy intuitively. According to the experimental results, our method is able to detect dispute couples of commenters about 79% on average. Also, we could find unusual commenters such as spammers or bursty commenters as well as a structure of controversy in comments. Keywords-Comment; Dispute; Weblog; Internet Discussion Board; Visualization;

I. I NTRODUCTION The Internet media such as blogs and web discussion boards have been the new space for public opinion. Many Internet portal sites are providing not only various the Internet discussion boards that people can use freely but also offer blog services so that an individual could make and use a blog easily. According to the status of blogosphere in 2007 reported by Technorati, the number of blogs is about seventy million and one hundred twenty thousand blogs have been created in a day [1]. 978-0-7695-4235-5/10 $26.00 © 2010 IEEE DOI 10.1109/CyberC.2010.90

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click the previous screen buttons page by page. Especially, in order to find dispute relation in the comments, we have no alternative but to read all comments. There has been several studies to extract an opinion or a controversy in comments or social networks [5], [6], [7], [8], [9], [10], [11], [12]. Since most of the previous methods focus on the contents of comments, they are dependent on the language used. Also, if there are some typos in comments, it may be difficult to apply these methods in such cases. In this paper, we propose a method for detecting and visualizing the dispute relation among commenters from the comments of Internet articles. Since comments written by disputing commenters tend to appear in turns, we can exploit the order of commenters to detect pairs of commenters in disputing. So, our method is not affected by the language used nor by the typos in comments. Also, we visualize the disputing relations by an undirected graph. It must be helpful to grasp the degree of controversy intuitively. The remainder of this paper is organized as follows. In Section 2, we review previous studies focused on extracting opinions from comments or visualizing comments. And we explain our method to detect disputing pairs of commenters and to visualize disputing relations in Section 3 and 4, respectively. We present the result of the experiments in Section 5 and conclude this paper in Section 6.

volume of comments in blog to the volume of a post. It is regarded that the larger the amount of comments belong to a post is, the higher the discussion level of the post becomes. However, there are many useless comments such as spam or duplicated comments with same contents. So, it is not always true that the more the amount of comments, the higher the level of disputes of them. DeMoor and Efimova have performed the argumentation analysis of blog conversations and found the potential of blog technologies [15]. They found that the articles over various blogs could incur the users’ frustration for keeping track of the discussions among the articles and the associated comments. They analyzed the reasons of these fragmentations but they did not perform any statistical analysis on the replying comments. Figure 1 shows the structure of a weblog conversation.

II. R ELATED W ORK Though the comments have not been much of concern of the most of users, there are some research results on blog articles and replying comments. Gumbrecht reported the importance of blog comments [2]. Trevino and Student also noted that the positive blog feedback can motivate the bloggers [3]. They reached to a similar conclusion that the replying comments are regarded by most bloggers as a vital facility on the interactive nature of blogs. In this section, we survey previous studies focused the contents or features of the comments attached to the Internet articles. Mishne and Glance presented a large-scale study of blog comments and their relation to the corresponding articles [13]. They suggested that comments constitute a substantial part of the blogosphere, accounting for up to 30% of the volume of blog articles themselves, and that the number of comments per article follows a power-law distribution. Also, they addressed the task of finding comment threads indicating a controversy as a text classification problem. This method classifies the comments using ‘disputative’ comments set trained a decision tree boosted with AdaBoost using a set of 500 manually annotated comment threads. So, the method is restricted within the trained languages. In 2007, Mishne introduced a new method for estimating the discussion level of a blog post from the volume of comments of the article [14]. In this method, the discussion level of a post is calculated to the ratio of the average

Figure 1.

A Representation of a Weblog Conversation [15]

It is hard to search and extract useful information from comments added to a post since most blog systems show articles and their comments in a form of a sequential list. Lee et al. proposed a visualization system TRIB to give a structured layout for the replying comments in a single screen shot [16]. TRIB considers the semantic weight between the subject article and corresponding comments using userdefined dictionaries, which provide various personalized views for a large set of comments. Figure 2 shows the comments visualization view using TRIB. In Figure 2, a comment is represented by a colored circle. Each of the red circles indicates that the corresponding comment is filtered out according to the keyword dictionary. Using this method, TRIB helps the users to classify the comments easily. M. Potthast and S. Becker introduce OPINIONCLOUD, a technology to summarize and visualize opinions that are expressed in the form of web comments [8]. In order to summarize of a set of comments, they use two dictionaries comprising human-annotated terms that are commonly used to express positive or negative opinions. Figure 3 shows the visualized result of OPINIONCLOUD. Figure 3 shows the summary contrasting positive and

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comments posted an article. The dispute probability of a commenter wi can be calculated using Equation 1. D(wi ) =

n  k=2

Figure 2.

1 dist(ri,k , ri,k−1 )

(1)

In Equation 1, ri,k represents the k th comment written by wi , and dist(ri,k , ri,k−1 ) means the distance between ri,k and ri,k−1 according to the order of comments. So, if a commenter rewrite the next comment adjacent to his/her previous comment, the dispute probability of the commenter increases. But, if more than two comments written by same commenter appear continuously, these comments are regarded as one comment. We calculate the dispute probability for all commenters who write more than 2 comments. And, we regard a commenter who gets a dispute probability over threshold as a disputing commenter. In order to detect disputing pairs of commenters, we calculate the dispute probability between wi and wj using Equation 2. m  n   1 max D(wi , wj ) = (2) dist(ri,k , rj,l )

A visualization result of TRIB [16]

k=1

l=1

In Equation 2, n and m indicates the number of comments written by wi and wj , respectively. The closer the comments written by wi and wj appear, the higher the dispute probability between wi and wj becomes. One may think that the bursty commenters, who wrote many comments repeatedly, generally have higher dispute probability than others. But, according to Equation 2, it is not always true because the comments written by them should be separated by other comments disputing with.

Figure 3. OPINIONCLOUD generated from the comments on a YouTube video. This cloud contrasts positive and . negative words [8]

negative terms on a YouTube video. As is customary for tag clouds, the font size of a term grows proportionally with its frequency in the comments.

IV. V ISUALIZATION OF D ISPUTING R ELATION In this paper, we visualize the dispute relation between commenters using undirected graph. In this graph, a commenter is represented by a node and an edge between two nodes means the two commenters represented by these nodes are in dispute. We can consider a few types of graph for dispute relations of commenters as shown in Figure 4. Figure 4(a) shows a dispute relation between two commenters. Figure 4(b) represents the case that some commenters (three commenters in this case) are disputing one another. Lastly, Figure 4(c) represents the case that one commenter is disputing with several commenters. In this case, we can regard that the author of the article disputes with commenters reading the article or a commenter is insisting that a black is white. In order to draw the dispute graph, we should detect the pairs of commenters having the dispute probability over a threshold. And then, the detected commenter nodes should be placed on proper positions. The positions of nodes are given to by the Kamada-Kawai algorithm that is a force

III. D ETECTION OF A D ISPUTING R ELATION IN A C OMMENT S ET Many readers of Internet articles may express their opinions about the articles by replying comments, as well as sometimes dispute with others who have different opinions. In fact, it is not difficult to find a set of disputing comments in weblogs or Internet discussion boards. Generally, a discussion manner in online community is similar to that in real world except writing a comment instead of talking one another. If a person writes a comment containing a contradictory opinion to the article or to the previous comments, the contradicted author or commenter may rewrite a comment in order to reinforce his/her own opinion. If some commenters keep up correspondence in this way, it is probable that the comments written by the commenters joining the discussion appear in turns. In this paper, we calculate the dispute probability of a commenter using the order of commenters in a set of

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A

Table I S AMPLE SEQUENCE OF COMMENTS

B (a) Dispute of 1:1

No. 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20

A

B

C (b) Dispute of n:n

B C

F

A

D

ID D B C B C B C B B C E D C D E C F G C E

No. 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40

ID F C C C F D A E C E E D C E C E C F E H

No. 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

ID C I C B C G C H H C E C G I B E B D G E

No. 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80

ID C B C B E H E B J B G E E E B L B E K B

No. 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100

ID D K L G E E E G E E G E G E G E G E G E

E

(c) Dispute of 1:n Figure 4.

Some typical types of dispute

Table II T HE NUMBER OF COMMENTS WRITTEN BY EACH COMMENTER . wi A B C D E F

directed layout algorithm [17]. The idea of a force directed layout algorithm is to consider a force between any two nodes. This algorithm is widely used in automatic graph drawing system such as Pajek [18]. In our visualization, a node is represented by a commenter’s id and the size of font becomes larger in proportion to the number of comments written by the commenter. The color of a node is selected at random. And the thickness of an edge is proportional to the strength of two nodes connected by the edge. Now, we consider a set of comments including 100 comments. And we detect and visualize the dispute relation of this comment set using the criteria presented in Section III. Table I represents this comment set. In Table I, ‘No.’ indicates the temporal index of the comment and ID represent the commenter’s ID writing the comment. The number of commenters is 12 and the number of comments written by each commenter is shown in Table II. In Table II, wi means the commenter’s ID and |R(wi )| means the number of comments written by wi . The commenter ‘A’ and ‘J’ have no dispute relation with other commenters because each of them wrote only one comment. All commenters except ‘A’ and ‘J’ stand a chance of a dispute. The dispute visualization of the comment set using our method is shown in Figure 5. As shown in Figure 5, there are seven pairs of dispute relation between commenters. From the thickness of the edge, we can guess that the commenter ‘E,’ ‘C,’ and ‘B’

|R(wi )| 1 15 22 7 28 4

wi G H I J K L

|R(wi )| 12 4 2 1 2 2

G

E B

C F

Figure 5.

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The visualization of dispute graph

that wrote many comments took an active part in dispute. Here, we need to consider the commenter ‘D’ and ‘F.’ As shown in Table II, the numbers of comments written by the commenter ‘D’ and ‘F’ are 7 and 4, respectively. Although the commenter ‘D’ writes more comments than ‘F,’ the node ‘D’ does not appear in Figure 5. But the node ‘F’ has an edge connected with the node ‘C.’ So, we may think that, if a commenter writes more comments, the dispute probability the commenter becomes the higher, but it is not always true.

V. E XPERIMENTAL R ESULTS In order to show the effectiveness and performance of our method, we conducted two types of experiments: the performance test on the detection of the dispute relation and the effectiveness test on the visualization. A. Detection of the dispute relation among commenters Though we read all comments added to a post, it is not easy to judge whether a comment is for controversy or not. So, to show the performance of dispute detection, we use the comments in ‘SkepticalLeft’ that is a popular forum site in Korea. In ‘SkepticalLeft,’ most users join in the controversy by writing comments. And when a user writes a comment to dispute with other commenter, he/she mainly specifies the ID of the commenter whom he/she wants to dispute with as shown in Figure 7.

In weblogs or Internet discussion boards, most commenters do not write many comments to the same post. It is especially true for the articles addressing no controversy issues. Such cases may be visualized as shown Figure 6.

B C D

A

(a) a commenter who wrote spam or duplicated comments

I

B

A

H G

F

C D E Figure 7. The comments attached to a post in ‘SkepticalLeft’ specifying the opposite commenter’s ID

(b) a commenter who tends to take pleasure in contradicting Figure 6.

As shown in Figure 7, a comment consists of writer’s ID, posting date, and the contents of the comment and this comment structure is similar to most weblogs or Internet discussion boards. In Figure 7, the part (a) indicates the IDs of writers writing the comments and the part (b) indicates the opposite commenters’ IDs with whom the commenter wants to dispute with. So we can find whether the comment is in controversy or not and who is the opposite commenter. We selected five articles with relatively many comments. Table III summarize the information of these articles. In Table III, |ri | and |wj | indicate the numbers of comments and commenters, respectively. And |ri |/|wj | indicates the average number of comments per commenter. These articles have more than 150 comments and the number

The case of an uncommon commenter

In Figure 6(a), since the size of ‘A’ is large and since ‘A’ has only one thin edge, we can guess that most comments written by ‘A’ bear little relation to controversy. For this reason, commenters being represented just like ‘A’ in Figure 6(a) are likely to be spammers who write spam or useless comments repeatedly. In Figure 6(b), we can see that the node ‘A’ has too many edges connected other nodes. It means that the commenter ‘A’ have a controversy with many commenters. In this case, we can guess that the comments written by ‘A’ can be intentional disturbance rather than real controversy.

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Table III T HE ARTICLES SELECTED FROM ‘S KEPTICAL L EFT ’

‘AGROA,’ ‘One Line Opinion,’ and ‘SkepticalLeft.’ Each site has different features supporting comments. First, ‘AGORA’ is the popular Internet discussion board served by ‘DAUM’ portal and the number of articles posted a day amounts to thousands [19]. There are not a few articles having more than hundreds comments, even though the spectrum is wide. Most comments in ‘AGORA’ tend to be short because ‘AGORA’ restricts the size of comments less than 300 characters. The next, ‘One Line Opinion’ is managed by ‘100 minutes discussion’ that is TV discussion program put out weekly [20]. The main article is presented weekly by the site manager and users can read it freely. But if a user want to write a comment, the user has to be verified by his/her real name. And every comments are tagged the writer’s real name. The length of comments in this site is also short just like ‘AGORA.’ Lastly, as mentioned above, ‘SkepticalLeft’ is a controversy-driven site so most comments in this site are involved in a dispute [21]. The length of comments tends to be long unlike that of ‘AGORA’ and of ‘One Line Opinion.’ And there are little spam or duplicated comments. We selected an article and comments posted to the article from each sites. Table V shows the information of the articles and the statistics of the comments posted to them.

FOR THE TEST OF

DISPUTE DETECTION

article S01 S02 S03 S04 S05

posting date 09.05.12 09.06.05 09.04.01 09.06.06 08.08.09

|ri | 218 172 167 155 164

|wj | 20 19 20 17 17

|ri |/|wj | 10.90 9.05 8.35 9.12 9.65

of commenters is about 17–20. The average number of comments per commenter is about 8–10. We first make a true set of pairs of commenters having a controversy for each article. And we compared it to the result of the dispute relation detected using our method as shown Table IV. Table IV T HE PERFORMANCE OF THE DETECTION CAPABILITY OF THE DISPUTE PAIRS OF COMMENTERS .

article S01 S02 S03 S04 S05

true-set 12 6 7 3 4

detected pairs 13 8 5 4 5

correct 7(58.33%) 4(66.67%) 5(71.43%) 3(100%) 3(100%)

false positive negative 6 5 4 2 0 2 1 0 1 0

Table V T HE TEST ARTICLES FOR VISUALIZATION OF DISPUTE RELATIONS

In Table IV, the ‘true-set’ indicates the number of pairs of commenters having a controversy actually and ‘detection pair’ indicates the result of detection using our method. The ‘correct’ column counts the numbers of the dispute pairs of commenters detected by our method within also are included in the ‘true-set.’ The ‘positive’ counts the pairs of commenters that were detected by our method but do not belong to the ‘true-set.’ Lastly, The ‘negative’ column counts the numbers of the dispute pairs of commenters that are not detected by our method even though they belong to the ‘true-set.’ The performance of dispute detection of our method is more than 66% except the article ‘S01.’ In the case of ‘S01,’ there were detected 7 pairs of commenters among 12 couples in ‘true-set,’ so the detection performance is about 58.33%. In this experiment, the degree of a controversy of most couples of ‘false positive’ and ‘false negative’ is not much strong. Though the detection rate of our method shows about 79% on average, most couples of commenters having strong dispute relation were detected. From the result of the experiment, we could detect the major controversy in comments using our method.

article P01 P02 P03

site AGORA One Line Opinion SkepticalLeft

[19] [20] [21]

|ri | 4,513 3,848 101

|wi | 3,000 1,760 7

|ri |/|wi | 1.50 2.19 14.43

In Table V, ‘P01,’ an article in ‘AGORA,’ has 4,513 comments written by 3,000 commenters. So, the average number of comments per one commenter is about 1.5. The article ‘P02’ is scrapped from ‘One Line Opinion’ that having 3,848 comments. And the number of comments averages about 2.19. The article ‘P03’ scrapped from ‘SkepticalLeft’ has 101 comments and 7 commenters are joined to writing comments. The number of comments per commenter averages about 14.43 which ranks much higher than those of ‘P01’ and ‘P02.’ It reflects the characteristics of ‘SkepticalLeft.’ We first detected the dispute pairs of commenters from comments of each articles then visualized them by our method. Figure 8 shows the visualization result for ‘P01.’ As shown in Figure 8, ‘P01’ is not much complex for an amount of comments. The node ‘jun’ has seven edges. It means that the commenter ‘jun’ disputes seven commenters. But, as mentioned in section IV, the commenter ‘jun’ is likely to be a spammer or a man who cause a disturbance intentionally. In fact, most comments written by ‘jun’ seem to be for increasing the hit counts of the ‘P01’ article. Figure 9 shows the visualizing result of ‘P02.’

B. Visualization of Dispute Relation To show the dispute structure in comments intuitively, we proposed a visualization method based on undirected graph. We collect some articles from three Internet forum sites,

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Figure 8.

The visualization of dispute relation of ‘P01’

Figure 10.

The visualization of dispute relation of ‘P03’

‘sonnet’ mainly dispute each others and most commenters except one commenter joins the controversy. The ‘Cato’ is also the author of ‘P03.’ From the result, we can find that our method can visualize the controversy structure of comments effectively. Furthermore, from the visualization result, we can intuitively catch the unusual commenters such as spammers or bursty commenters without reading all comments. VI. C ONCLUSION

Figure 9.

In this paper, we proposed the method for detecting and visualizing the dispute relations among commenters for an Internet article. We defined the dispute probability between the commenters using the temporal order of comments written by them. Therefore, our method is not affected by the language used. Also, the controversy relation is visualized using an undirected graph. The visualization result is helpful to grasp the degree of controversy intuitively. According to the experimental results, our method is capable to detect dispute pairs of commenters in degree of accuracy about 79% on average. Also, we could find unusual commenters such as spammers or bursty commenters as well as the structure of controversy. Our method detects the dispute pairs of commenters regardless to the contents of comments, so our method is robust in the sense that it is affected neither the language used nor the typos. In the future, we are planning to extend our system to be able to classify the dispute pairs of commenters into opinion groups.

The visualization of dispute relation of ‘P02’

The dispute graph of ‘P02’ seems to be more complex compared with that of ‘P01.’ In Figure 9, we can see that the node ‘Kyungmin Lee’ and ‘Ho Jung’ do not have any edge connected other nodes though the size of node is large. That means the comments written by them are not for a controversy. Judging from this fact, they are likely to write spam or duplicated comments. In fact, most comments written by them are duplicated comments containing same contents. Lastly, Figure 10 shows the visualization result of ‘P03.’ From Figure 10, we can find that the ‘Cato,’ ‘Banny,’ and

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ACKNOWLEDGMENT

[16] Y. Lee., M. Bae, G. Woo, and H. Cho, “A Personalized Visualizing and Filtering System for a Large Set of Responding Messages on Internet Discussion Forums,” in Proceedings of the CIT09, vol. 2. IEEE Computer Society, 2009, pp. 160– 165.

* Corresponding should be addressed to Gyun Woo (Email: [email protected]) R EFERENCES

[17] T. Kamada and S. Kawai, “An algorithm for drawing general undirected graphs,” Information processing letters, vol. 31, no. 1, pp. 7–15, 1989.

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