Connotation and Determinants of Web Collective Intelligence

Lingling ZHANG, Hanbin TANG, Minghui ZHAO

Journal of Systems Science and Information ›› 2022, Vol. 10 ›› Issue (6) : 633-644.

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Journal of Systems Science and Information ›› 2022, Vol. 10 ›› Issue (6) : 633-644. DOI: 10.21078/JSSI-2022-633-12
 

Connotation and Determinants of Web Collective Intelligence

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Abstract

With continuous development of network technology, users in network community are promoted to interact deeply, and remarkable web collective intelligence emerges in the process. As a relatively new concept, the connotation of web collective intelligence is preliminarily explored in this paper, where the network community is taken as the environment, expert users as the subject, and web comments as the carrier. Meanwhile, taking Wikipedia as an example, by means of questionnaire survey and structural equation model, a more systematic index system is constructed from the perspective of user characteristics to explore determinants of web collective intelligence quality, and potential influence of user attributes on user behavior.

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web collective intelligence / connotation / determinants

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Lingling ZHANG , Hanbin TANG , Minghui ZHAO. Connotation and Determinants of Web Collective Intelligence. Journal of Systems Science and Information, 2022, 10(6): 633-644 https://doi.org/10.21078/JSSI-2022-633-12

1 Introduction

After careful observation of bees, ants and other biological groups, biologists have found that they have significant group behaviors. They can accomplish complex tasks that individuals can't solve through cooperation, which shows the existence of biological collective intelligence, and also guides researchers to pay attention to the collective intelligence in human society. Collective intelligence is self-organizing and self-adaptive, and individuals can change their behavior according to specific principles to achieve goals of the group[1, 2]. Surowiecki published the landmark book "The Wisdom of Crowds", arguing that group are usually smarter than the smartest person in a specific environment[3]. Leimeister believes that collective intelligence refers to group members using their personal knowledge to put forward different methods to get better solutions for a specific problem[4].
Network community plays an important role in obtaining needed information, expanding interpersonal communication, encouraging social attention and providing life services. With the development of computer technology and network technology, the paradigm of information creation, information communication and information consumption is undergoing profound changes. User interaction can break the block of time and space through network community, and from group behaviors emerge multi-structure and multi-level collective intelligence, which is the web collective intelligence studied in this paper. Malone believes that key problems of web collective intelligence are how to deeply combine human with computers, so that the generated web collective intelligence is significantly superior to the ability of human individuals, human groups and computers[5].
Compared with traditional expert collective intelligence, web collective intelligence has obvious advantages such as large participation scale, network data driven and strong network effect. As a relatively new concept, web collective intelligence is preliminarily explored in this paper, where network community is taken as the environment, expert users as the subject and web comments as the carrier. Taking Wikipedia as an example, by means of questionnaire survey and structural equation model, this paper explores determinants of the web collective intelligence quality from the perspective of user attribute characteristics and behavior characteristics. Compared with previous studies, this paper innovatively increases behavior characteristics and constructs a more systematic determinants system. It is verified that the user diversity has a significant positive impact on user behavior, and indirectly has a positive impact on the web collective intelligence quality.

2 Connotation of Web Collective Intelligence

Although the research on collective intelligence is relatively extensive, the research on web collective intelligence is scarce. Wang et al. think that web collective intelligence is the group ability to solve complex problems or specific tasks, which is characterized by large-scale online collaboration in a deeply interactive network[6]. Zhuang, et al. think that many individual units with independent cognition communicate, cooperate, solve problems and perform tasks through the network community, thus embodying the web collective intelligence[7].
Web collective intelligence can be divided into two categories: Explicit wisdom directly output by network users and implicit wisdom indirectly output by network users. Explicit wisdom mainly refers to the content that users directly provide to network community, including posting, asking, answering, commenting, scoring, voting, recommending, labeling, etc. Implicit wisdom mainly refers to the wisdom gained from data analysis, information aggregation and knowledge generation, which mainly exists in the form of unstructured text. Compared with the traditional web collective, there are significant differences between them[6, 8].
Table 1 Comparison between web collective intelligence and traditional collective intelligence
Type Differences Similarities
Web Collective Intelligence Focus on the social environment, large-scale participation, network data driven, strong network effect, great complexity and uncertainty. Self-organization and self-recovery, no centralized control and follows simple rules.
Traditional Collective Intelligence Focus on the natural environment, observation records driven and weak network effect.

2.1 Environment of Web Collective Intelligence: Network Community

Network community is a platform for knowledge exchange and knowledge creation based on the Internet. Knowledge and knowledge, users and users, and knowledge and users form extensive interaction and association. Network community breaks the limitation of space and time, making the gathering and interaction of large-scale expert users more convenient. Users express their views on social, economic and technological issues in different forms. The user-generated data and domain knowledge can be accumulated quickly, and web collective intelligence is constantly emerging through network interaction[9]. From the three dimensions of user diversity, user interaction and knowledge complexity, network community has generally gone through four stages. User diversity has gradually increased, user interaction has become more frequent, knowledge contained has become more complex, users' willingness to express has become stronger, and quality of generated content has become higher[10].
Table 2 Community development stage
1.0 2.0 3.0 4.0
User Diversity Low Medium High Medium
User Interaction Low Medium High High
Knowledge Complexity Low Medium High Extremely High
Typical Example BBS, Blog Yahoo Answers, Wikipedia Zhihu, InnoCentive NewsFuture, ScienceNet

2.2 Subject of Web Collective Intelligence: Expert Users

The vast number of users in technological network community have different user attributes and user behaviors. Some of them make great contributions to the web collective intelligence, while some of them make little contributions. Through multi-dimensional user portraits, users can be divided into three types: General experts, domain experts and authoritative experts. General experts refer to people who can express their personal feelings, opinions, thoughts and experiences about problems in a certain field. Domain experts refer to people who have worked in related fields for a long time and conducted systematic research. Authoritative experts refer to those who have been engaged in long-term research in related fields, who have not only conducted in-depth exploration in basic theory, but also carried out extensive verification in practical application, such as senior professors, famous Chinese medicine practitioners and senior technicians[11].

2.3 Carrier of Web Collective Intelligence: Web Comments

Web comments refer to the information with opinions shared on the network. In this paper, it refers specifically to various interactive behavior data of users in the open network community[12, 13]. Web comments are mainly in the form of short texts, which include structured data such as login time, login frequency and score level, semi-structured data such as browsing articles, subscribing to collections, and paying attention to friends, and unstructured data such as questions, answers, comments and replies. Technological network community has the characteristics of certain professional threshold, wide participation groups, strong immediate interaction. In the process of network interaction, users externalize their own tacit knowledge into explicit knowledge, and web comments are a good intermediate carrier for this knowledge transformation process.

3 Determinants of Web Collective Intelligence Quality

As a typical product of web collective intelligence, Wikipedia entries are provided, edited and revised by vast number of users, thus forming an anonymous, obligatory and open platform. Taking Wikipedia as an example, by means of questionnaire survey and structural equation model, this paper studies determinants of web collective intelligence quality from the perspective of user characteristics, and innovatively explores the possible influence of user attributes on user behavior. From previous studies, it can be found that among determinants of web collective intelligence quality, user characteristics can be divided into attribute characteristics and behavior characteristics, mainly including quantity, diversity, professionalism, cooperation, conflict, knowledge, cultural background, etc[14].
Table 3 Variable and definition of user characteristics
Variable Definition Reference
Attribute Characteristics Quantity Number of users in the community who contribute wisdom. [15, 16]
Diversity Users can be divided into different types due to their different background, knowledge, experience, and motivation. [17]
Professionalism Users can express personal views on specific issues according to their knowledge in specific fields. [18, 19]
Behavior Characteristics Cooperation and Conflict When faced with the same question, users may cooperate and complement each other to form a unified answer, or they may have different opinions and cause conflicts and confrontations. [20, 21]
Web Collective Intelligence Quality Accuracy, timeliness, relevance and completeness. [22, 23]

3.1 Theoretical Model and Hypotheses

User attribute characteristics mainly include the quantity, diversity, and professionalism, all of which are closely related to the web collective intelligence quality. Quantity refers to the number of users who contribute wisdom in the network community. The network community breaks the limitation of time and space, so that large-scale users show better mobility and creativity through deep interaction, thus generating high-quality web collective intelligence. Diversity means that users can be divided into different types due to their different background, knowledge, experience, and motivation. Especially when users are faced with complex problems, homogeneity may curb the generation of innovative thinking, but solving problems requires diverse users to provide diverse perspectives. Professionalism means that users can express personal views on specific issues according to their knowledge in specific fields. If you ask questions to users with relevant knowledge in the network community, you will find that problems are often solved quickly, and scattered, useless and unorganized information is thus avoided.
Javanmardi et al. found that the number of users, especially registered users in Wikipedia community, had a significant positive relationship with the web collective intelligence quality[24]. Kane analyzed 188 similar articles in Wikipedia, and explored the influence of user quantity, user diversity, anonymous user number and top user experience on high-quality articles. Kane found that all of them had significant positive effects except the user quantity[25]. According to user data of Wikipedia, Huang studied the influence of user characteristics on web collective intelligence quality by using Agent technology, and found that the increase of quantity could improve the completeness of articles[16]. Through the research on 4317 articles quality in Wikipedia film community, Robert, et al. found that the user diversity and user professionalism were crucial to the articles quality[15].
Joo et al. collected 691 responses from Wikipedia and other knowledge network in South Korea and United States, and found that the quantity, diversity, and professionalism of users were the decisive determinants of content quality[23]. Holtz, et al. assumed that user professionalism had a positive effect on the quality of health-related Wikipedia articles, and verified that there was a significant positive correlation between them by nonparametric Mann-Whitney U-tests[18]. Tang crawled data from Wikipedia and academic encyclopedia, and verified that the quantity, activeness and professionalism of users had a significant positive relationship with the articles quality. Tang also found that user professionalism was the decisive factor for obtaining high-quality articles, while the quantity and activeness could make up for shortcomings caused by the lack of professionalism[19]. Large-quantity, strong-activeness and low-professionalism users were more likely to produce high-quality results than small-quantity, low-activeness and high-professionalism users. Based on the above literature review, the following hypotheses are put forward:
H1: There is a positive correlation between the user quantity and the web collective intelligence quality.
H2: There is a positive correlation between the user diversity and the web collective intelligence quality.
H3: There is a positive correlation between the user professionalism and the web collective intelligence quality.
User behavior characteristics mainly include cooperation, interaction, competition and conflict, all of which are closely related to the web collective intelligence quality. When faced with the same question, users may cooperate and complement each other to form a unified answer, or they may have different opinions, resulting in conflicts and confrontations. Cooperation and conflict can also be transformed into each other. Conflicts may arise in the process of cooperation. Conflicts can also achieve more effective cooperation by reaching consensus. Finally different user behaviors can improve the information content, details, integrity and other aspects of web collective intelligence.
With users as nodes and interactions as edges, Zhao, et al. analyzed the text of high-quality Wikipedia articles, and constructed a cooperative interaction network. Zhao, et al. found that the cooperation intensity, cooperation times and user diversity were closely related to the articles quality[26]. According to Wikipedia dataset, Zuo built a cooperative network to analyze the influence of user cooperation on the articles quality, and found that the user quantity, cooperation methods, cooperation closeness had a significant positive impact on the articles quality, and user groups' contribution to articles quality was greater than any individual[20]. Huang, et al. selected interactive behaviors in comments for content analysis, and found that actions with stronger cooperation were more likely to get replies. Thirteen types of cooperation between users had a significant positive impact on the articles quality, especially on the information content, completeness and details[27]. Chang thought that many users of Wikipedia had different opinions on a certain entry, which led to conflicts. The source data related to conflicts were extracted from the editing history of Wikipedia, Chang found that the resolution of conflicts could improve the accuracy of entries[28].
Du found through the research on Wikipedia that user diversity could increase cognitive differences and cooperative interactions, thus promoting the web collective intelligence quality[17]. Liu, et al. built a conflict impact model based on social cognitive theory, and made an empirical test on 364 English Wikipedia entries by using analytic hierarchy process, and found that there was a positive correlation between user diversity (specifically referring to knowledge heterogeneity) and conflict[21]. Based on the cognitive theory of interactive team, Qiu explored the influence of team heterogeneity and interactive process on knowledge generation, and found that user diversity (specifically referring to team heterogeneity) had a positive impact on user conflict[29]. Arazy, et al. explored the impact of user diversity, task-related conflicts, and different roles on the articles quality, and found that diversity could bring higher creativity, which in turn had a positive impact on the articles quality[30]. Based on the above literature review, the following hypotheses are put forward:
H4: There is a positive correlation between the user cooperation and conflict and the web collective intelligence quality.
H5: There is a positive correlation between the user diversity and the user cooperation and conflict.
Figure 1 Theoretical model and hypotheses

Full size|PPT slide

3.2 Questionnaire Design and Test

In this paper, Likert five-point questionnaire was adopted. On the one hand, the variables were designed from expert interviews. On the other hand, the relatively mature questionnaires in previous studies were referred to ensure the comprehensiveness and accuracy of variables. After screening, optimizing and adjusting, the questionnaire of determinants of web collective intelligence quality is finally designed, which contains 3 exogenous latent variables, 2 endogenous latent variables and 19 measurement questions.
There is still a gap between Wikipedia and professional library in function, but Wikipedia has become the mainstream source of introductory and supplementary information for most teachers and students, so they have a more direct contact and deep feelings about Wikipedia[22]. The research object is mainly teachers and students in colleges, and the research tool is Wenjuanxing. The research time is from December 14th to December 26th, 2020. The research method is the combination of online social platform and offline paper questionnaire. Finally, a total of 314 valid questionnaires with high quality are screened and collected.
In this part, SPSS and AMOS software are used to calculate the reliability and validity of the questionnaire data. The reliability mainly examines comprehensive reliability (CR) and Cronbach coefficient (α), and the validity includes aggregation validity and discrimination validity. The aggregation validity examines standard factor load and average extraction variation (AVE), and the discrimination validity examines chi-square (χ2), degree of freedom (DF), χ2/DF, RMSEA, RMR, etc. From Table 5 and Table 6 we can find that factor load is basically greater than 0.7, AVE is all greater than 0.5, CR and CI are basically greater than 0.8, and all fitting indexes of factor model are worse than the original model. To sum up, the theoretical model has good reliability, aggregation validity and discrimination validity.
Table 4 Questionnaire of determinants of web collective intelligence quality
Variable Encode Question Reference
Quantity(SL) SL1 The number of editors is important for the information quality. [15]
SL2 The more editors, the more information they can contribute.
SL3 Wikipedia has a sufficient number of editors who can provide information on issues in different fields.
Diversity(DY) DY1 A variety of editors can provide information from different angles. [17]
DY2 Editors have different ages.
DY3 Editors have different educational backgrounds.
DY4 Editors have different working experiences.
Professionalism(ZY) ZY1 Editors have enough knowledge about the edited entries. [18]
ZY2 Editors usually choose to edit entries with relevant background knowledge.
ZY3 Editors have enough editing experience.
Cooperation and Conflict(HC) HZ1 The cooperation between editors is important for information quality. [20]
HZ2 The cooperation form between editors is important for information quality.
HZ3 The cooperation intensity between editors is important for information quality.
CT1 Different editors will have different opinions on the same entry, which leads to conflicts. [19]
CT2 The conflict between different editors is important for information quality.
CT3 Different editors reach a consensus through conflicts is important for information quality.
Web Collective Intelligence Quality (ZL) ZL1 The information provided by Wikipedia is reliable.
ZL2 The information provided by Wikipedia is accurate. [23]
ZL3 The information provided by Wikipedia is new.
Table 5 Aggregation validity and reliability
Variable Encode Factor Loading AVE CR α
SL SL1 0.774 0.563 0.794 0.793
SL2 0.748
SL3 0.728
DY DY1 0.786 0.620 0.866 0.855
DY2 0.842
DY3 0.851
DY4 0.655
ZY ZY1 0.865 0.641 0.842 0.833
ZY2 0.822
ZY3 0.706
HC HZ1 0.784 0.575 0.890 0.889
HZ2 0.780
HZ3 0.801
CT1 0.672
CT2 0.742
CT3 0.764
ZL ZL1 0.802 0.680 0.864 0.860
ZL2 0.876
ZL3 0.794
Table 6 Discrimination validity
Factor Model χ2 DF χ2/DF RMR CFI IFI RMSEA
Original Model 418.6 141 2.969 0.036 0.923 0.924 0.079
Four-Factor Model 631.1 146 4.322 0.045 0.865 0.866 0.103
Three-Factor Model 775.3 149 5.203 0.051 0.826 0.827 0.116
Two-Factor Model 921.8 151 6.104 0.055 0.786 0.787 0.128
Single Factor Model 1167.9 152 7.683 0.060 0.718 0.719 0.146

3.3 Experimental Results and Suggestions

Under the condition that χ2/DF, RMR, CFI, IFI, RMSEA meet the requirements, path analysis results show that the quantity and the professionalism have a significant positive impact on the web collective intelligence quality, the cooperation and conflict have a positive impact on the web collective intelligence quality, the diversity has a significant positive impact on the cooperation and conflict, the diversity has no significant impact on the web collective intelligence quality, but it can indirectly affect the web collective intelligence quality by influencing the cooperation and conflict. Finally hypotheses H1, H3, H4, H5 pass the test. Attribute characteristics and behavior characteristics of users can improve the web collective intelligence quality.
Table 7 Model fitting condition
χ2/DF RMR CFI IFI RMSEA
Actual value 2.975 0.041 0.923 0.923 0.079
virtual value < 3 < 0.05 > 0.9 > 0.9 < 0.08
Table 8 Path coefficient and hypothesis test results
Hypotheses Coefficient P-Value Result
H1: SL→ZL 0.567 < 0.001 Positive
H2: DY→ZL -0.352 0.075 Negative
H3: ZY→ZL 0.386 < 0.001 Positive
H4: HC→ZL 0.333 0.021 Positive
H5: DY→HC 0.833 < 0.001 Positive
Figure 2 Structural equation model and path coefficient

Full size|PPT slide

Specifically, the quantity and professionalism of users have a positive impact on the web collective intelligence quality, which is the same as the findings of most previous studies. When there are complex problems, it is easier to get accurate solutions by summarizing the opinions of large-scale users. Asking users for solutions in professional communities can filter noise information and solve problems quickly. The specific impact of user diversity has not been formally verified, and there are still different opinions on homogeneity and diversity in previous studies. When users are faced with the same problem, their diverse opinions may lead to multi-dimensional solutions, but the scattered opinions may also lead to answers that deviate from the right direction.
The above indicators all belong to user attribute characteristics. This paper innovatively increases the user behavior characteristics to construct a more systematic determinants system, and verifies that the user diversity has a positive impact on the user cooperation and conflict, and indirectly has a positive impact on the web collective intelligence quality. User diversity can increase cognitive differences and cooperative interactions among users, while cooperation and conflict can transform into each other. Conflicts may arise in the process of cooperation, and conflicts may achieve more effective cooperation through reaching consensus, thus having a positive impact on the web collective intelligence quality from the aspects of detail and integrity.

4 Conclusion

This paper first expounds the connotation, environment, subject and carrier of the web collective intelligence. In terms of environment, network community enables users to generate data and accumulate knowledge, and web collective intelligence is constantly emerging through network interaction. In terms of subject, users in the network community can be divided into general experts, domain experts and authoritative experts according to user portraits. In terms of carrier, when expert users externalize their tacit knowledge into explicit knowledge, web comments are the intermediate carrier in the process of knowledge transformation. Taking Wikipedia as an example, by means of questionnaire survey and structural equation model, this paper then studies the determinants of the web collective intelligence quality from two aspects: User attribute characteristics and behavior characteristics. Experimental results show that the user quantity and the user professionalism have a significant positive impact on the web collective intelligence quality, and the user diversity has a significant positive impact on the user cooperation and conflict, and indirectly has a positive impact on the web collective intelligence quality.

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