Chapter 1 Introduction Man is by nature a social animal — Aristotle, Politics Contents 1.1 Issues of collaborative systems .2 Trust as Research Topic .1 Should we introduce trust score to users? .2 How do we calculate the trust score of partners who collaborated? .3 How do we predict the trust/distrust relations of users who did not interact with each other? .1 Studying user trust under different circumstances with trust game .2 Calculating trust score .3 Predicting trust relationship .1 Studying influence of trust score on user behavior .2 Designing trust calculation methods .3 Predicting trust relationship .1 Research Context Collaboration is defined in Oxford Advanced Learner’s Dictionary as “the act of working with another person or group of people to create or produce something” [Sally et al. Human societies might not have been formed without collaboration between individuals. Human need to collaborate when they can not finish a task alone [Tomasello et al. Introduction Hill, a social anthropologist at Arizona State University, stated that “humans are not special because of their big brains.
That’s not the reason we can build rocket ships – no individual can. We have rockets because 10,000 individuals cooperate in producing the information” [Wade, 2011]. Collaboration is an essential factor for the success in the 21st century [Morel, 2014]. Before the Internet era, collaboration was usually formed within small groups whose members were physically co-located and knew each other.
Studies [Erickson and Gratton, 2007] argued that in 20th century “true teams rarely had more than 20 members”. According to the same research study, today “many complex tasks involve teams of 100 or more”. Collaboration from distance is easier for everyone thanks to the Internet. Collaborative systems are the software systems which allow multiple users to collaborate.
Some collaborative systems today are collaborative editing systems. They allow multiple users who are not co-located to share and edit documents over the Internet [Lv et al. The term “document” can refer to different kinds of document such as a plain text document [Gobby, 2017], a rich-text document like in Google Docs [Attebury et al., 2013], a UML diagram [Sparx, 2017] or a picture [J. Tang and Minneman, 1991].
Other examples of collaborative systems are collaborative e-learning systems where students and teachers collaborate for knowledge sharing [Monahan et al. The importance of collaborative systems is increasing over recent years. An evidence is that the collaborative systems attract a lot of attention from both academy and industry, and their number of users has increased significantly over time. For example, we display the number of users of ShareLatex, a collaborative Latex editing system, over last five years in Figure 1.
The number of users of ShareLatex increases rapidly. Zoho1 - a collaborative editing system similar to Google Docs - achieved the number of registered users of 13 millions [Vaca, 2015]. The number of authors who collaborated in scientific writing has increased over years as displayed in Figure 1. Collaboration is more and more popular in scientific writing [Jang et al., 2016; Science et al.
Version control systems like git and their hosting services such as Github became de-facto standard for developers to share and collaborate [Gerber and Craig, 2015]. In April 2017, Github has 20 millions registered users and 57 millions repositories [Firestine, 2017]. In traditional software systems such as Microsoft Office2 , users use and interact with the software system only. In collaborative systems, user need to interact not only with the system but also with other users.
Therefore, the usage of collaborative systems raises several new issues that will be discussed in the next section. In the following section we discuss about the new issues of collaborative systems. Then we discuss about trust between human in collaboration as our research topic. Afterwards we formalize our research questions, present related studies and our contributions for each research question.1 Issues of collaborative systems In a collaborative system, a user needs to use the system and interact with other users called partners in this thesis.
Studies [Greenhalgh, 1997] indicated several problems in developing collaborative systems. These problems are similar with problems in developing traditional software systems, such as designing a user interface for collaborative systems [J. Tang and Minneman, 1991; Dewan and Choudhary, 1991], improve response time [R. Kraut et al., 1992] or designing effective merging algorithms that combine modification of users [C.
Ignat et al. Collaborative systems 1 https://www.com/ 2 We refer to the desktop version, not Office 365 where users can collaborate online. Research Context Figure 1.1: Number of ShareLatex’s users over years. like Google Docs are widely used in small-scale [Tan and Y.
Surveys and user experiments [Edwards, 2011; Wood, 2011] claimed the positive perception from Google Docs users. However, in collaborative systems, users interact with their partners to finish tasks. We assume that the main objective of a user is to finish tasks at the highest quality level. The final outcome depends not only on the user herself but also all her partners.
If a malicious partner is accepted to join a group of users and is able to modify the shared resource, she can harm other honest users. We define malicious users as users performed malicious actions. The malicious actions can take different forms in different collaborative systems. In Wikipedia, malicious users can try to insert false information to attack other people or promote themselves.
These modifications are called vandalism in Wikipedia [Potthast et al. In source-code version control system such as git, malicious users can destroy legacy code or insert virus into the code [B. Chen and Curtmola, 2014]. Git supports revert action but it is not easy by non-experienced users [Chacon and Straub, 2014].
In collaborative editing systems such as ShareLatex, a malicious user can take the content written by honest users for an improper usage, such as to use the content in a different article and claim their authorship. Alternatively, if a user collaborates with honest partners, they can achieve some outcomes that no individual effort can. The claim has been confirmed by studies in different fields [Persson et al., 2004; Choi et al., 2016], such as in programming [Nosek, 1998] or in scientific research [Sonnenwald, 2007]. For instance, it is popular in scientific writing today that a scientific article is written by multiple authors [Science et al., 2017; Jang et al., 2016] because each author holds a part of the knowledge which is needed for the article.
If they can collaborate effectively together they can produce a scientific publication. Otherwise each of them only keeps a meaningless piece of information. In collaborative software development, it is often that 3 Chapter 1.2: Average number of collective author names per MEDLINE/PubMed citation (when collective author names present). Image source: [Science et al.
developers in the team have expertise in a narrow field. For instance a developer has experience in back-end programming while another developer only has knowledge in user interface design and implementation. If these two developers do not collaborate with each other, none of them can build a complete software system. In collaborative systems, a user decides to collaborate with a partner or not by granting some rights to the partner.
For instance, in Google Docs or ShareLatex, the user decides to allow a partner to view and modify a particular document or not. In git repositories, the user decides to allow a partner to view and modify code. The user needs to make a right decision, i. to collaborate with honest partners and not with malicious ones.
However, we only can determine malicious partners if: • Malicious actions have been performed. • The user is aware about the malicious actions. For instance, the user needs to be aware about the actions, or the direct or indirect consequences of the actions. If the user is aware of a potential malicious action, she also needs to decide if this action is really a malicious action or just a mistake [Avizienis et al.
Therefore, usually a single harmful action is not enough to determine one partner as a malicious partner. As an example, suppose Alice collaborates with Bob and Carol. Bob is a honest partner and Carol is a malicious one. However, so far both Bob and Carol collaborated and none of them performed any malicious activity.
The malicious action is only planned inside Carol’s mind. In this case, there is no way for Alice to detect Carol as a malicious user unless Alice can read Carol’s mind which is not yet possible at the time of writing [Poldrack, 2017]. Furthermore, if Carol performed the malicious action but the result of this action has not been revealed to Alice, Alice also cannot detect the malicious partner. Unfortunately, it is usual in collaborative systems that the user can reveal the result of a malicious action after a long time.
In some cases, the results will never be revealed. Research Context Suppose Alice is a director of an university and she inserted a wrong information into Wikipedia to claim that her university is the best one in the continent with modern facili- ties and a lot of successful students. The result might be that the university attracts more student, receives more supporting fund or be able to recruit better researchers - but these re- sults might take a long duration or even are impossible to reveal. As of this writing, it is not easy to detect wrong information automatically [Y.
Zheng et al. Some Wikipedia editors received money to insert wrong or controversial information [Pinsker, 2015]. The bad outcomes might also come from the fact that partners lack competency, i. they do not have enough information or skill to finish the task with an expected quality.
For instance, a developer might insert an exploiting code without intention. It might be difficult to distinguish whether the action was malicious. However as we discuss in Section 1.2, a user might not need to distinguish a malicious action from an unintended one. The reason is that trust reflects the user expectation that a partner adopts a particular kind of behavior in the future.
Hence the user has to decide to collaborate with a partner or not with some uncertainty about future behavior of this partner. Moreover the results of future behavior are also uncertain. In other words, there is risk in collaboration. To start the collaboration, the user needs to trust their partner at a certain level.2 Trust as Research Topic Studies claimed that trust between humans is an essential factor for a successful collaboration [Mertz, 2013].
[Cohen and Mankin, 1999, page 1] defined virtual teams as team “composed of geographically dispersed organizational members”. We can use the definition to refer to the team who collaborate using a collaborative system over the Internet and some members of the team do not know each other. [Kasper-Fuehrera and Ashkanasy, 2001; L. Peters and Manz, 2007] claimed that trust is a vital factor for the effectiveness of the virtual teams.
Because trust is a common and important concept in different domains, the term has been defined in different ways and there is no wide-accepted definition [Rousseau et al., 1998; Cho et al. In psychology, trust is defined as “an expectancy held by an individual that the word, promise, verbal or written statement of another individual can be relied upon” [Rotter, 1967, page 651] or “cognitive learning process obtained from social experiences based on the conse- quences of trusting behaviors” [Cho et al. [Rousseau et al., 1998, page 395] reviewed different studies on trust and proposed a definition of trust as “a psychological state comprising the intention to accept vulnerability based upon positive expectations of the inten- tions or behavior of another”. The definitions of [Rotter, 1967] and [Rousseau et al., 1998] focus on the future expectation of trust, while the definition presented in [Cho et al., 2015] focused on the historical experience of trust: trust is built based on observations in the past.