5.3.1. SignaComm’s context

In general terms, the SNS interface follows the universal design principles of simplicity, flexibility and accessibility of use (Spiliotopoulos et al. 2013; Chandler 2017). In addition, an SNS interface is graphical and contextual (Morin et al. 2012; Jain et al. 2013). Its graphical nature is based on a template that meets the already defined and precise rules to ensure homogeneous and uniform results. These rules are the following: a simple and figurative content, uniqueness of graphical representations and uniqueness of color contents (Kordahi 2013a). As for the dictionary of emoticons, it contains emotion symbols that are used worldwide (Alloing and Pierre 2017).

So far, we have not found published works regarding the standardization of visual communication systems for SNS. A number of companies have developed their own communication systems to integrate it into the SNS interface (e.g. the graphical user interface of WhatsApp) and new technology tools (e.g. the graphical user interface of Apple iPhone). The company’s (or organization’s) aim may be to intuitively guide users in their actions in entirely different and various contexts (Jain et al. 2013). Each company (or organization) chooses to adapt the charter of its visual communication system and its corresponding tools (e.g. SNS and new applications) according to the targeted countries. This adaptation approach may include the countries’ laws, cultures, customs and traditions. Social media applications (the graphical user interface and emoticon dictionaries included) are essentially altered for two reasons. Firstly, to be compatible with international standards and regulations defined by every country’s government, and secondly, to meet the universal design principles depending on users’ cultures.

To fulfil its objective, the SignaComm for the protection of personal data should use the signage system (Kordahi 2013a), as well as the graphical and contextual interface (Morin et al. 2012; Jain et al. 2013). The latter should meet the universal design principles (Spiliotopoulos et al. 2013; Chandler 2017). We have chosen both criteria to ensure the SignaComm’s perception and spontaneous understanding worldwide. Furthermore, the SignaComm’s development is in relation to three main concepts: the signage system (Kordahi 2013a), new SNS technology tools (Boyd and Ellison 2007; Ellison and Boyd 2013) and the user’s adaptation. This interrelation makes the SignaComm dependent on these social, technical and human environments. For now, we will not include the emotional aspect as this SNS is an informative one.

5.3.2. SignaComm’s pattern

The pattern for the SignaComm holds a network of connections between large and small patterns. In this work, we will present 12 large and two small patterns (in total 14 patterns) (Alexander 1977; Gamma et al. 1995). The description is divided into several stages. A diagram will follow the explanations (see Figure 5.2).

We start with pattern 1 (larger environments), which refers to many environments influencing the growth of SNS, such as national legal systems, Internet governance, Internet security threats, information and communication technologies, architecture design, as well as socio-cultural environments (Boyd and Ellison 2007; Ellison and Boyd 2013). These larger environments belong to the social sciences, applied sciences, formal sciences and humanities disciplines.

Pattern 2 (virtual communities environment) is contained inside pattern 1. A virtual community is an information system with a social network of members (Wellman 2002). The interactions between users can be nominative (e.g. between two specific individuals) or grouped (e.g. between a user and a group). Virtual community users can belong to various geographic locations, cultures, age groups and social groups (Wellman 2002). A virtual community’s aim is to allow a group of users to communicate randomly and worldwide (Fernback 1997; Wellman and Gulia 1999) (e.g. exchange learning resources, play). Pattern 2 holds pattern 10 (SignaComm community). Patterns 1 and 2 will influence pattern 10’s development and growth (Boyd and Ellison 2007; Raine and Wellman 2012; Ellison and Boyd 2013).

Pattern 10 is designed based on the SNS’ characteristics (Boyd and Ellison 2007; Raine and Wellman 2012; Ellison and Boyd 2013). It contains and describes the SignaComm functionalities (pattern 11), information technology administration (IT administration) (pattern 40) and interface (pattern 100). As pattern 10 is contained inside patterns 1 and 2, it interacts with both of them (Boyd and Ellison 2007; Raine and Wellman 2012; Ellison and Boyd 2013).

Pattern 11 has various functionalities, listed as follows: the automatic translation of syntagms into signagrams (pattern 22), signage and signagrams models (pattern 23), natural languages and linguistic rules (pattern 24), dictionary (pattern 25), ontology (pattern 26), user profile characteristics and members’ list (patterns 20 and 21), activities (pattern 30) and privacy (pattern 31) (Boyd and Ellison 2007; Raine and Wellman 2012; Ellison and Boyd 2013; Kordahi 2013a). Every functionality has its own programming functions. It is activated instantly according to member’s or user’s requests. Some functionalities are dynamically synchronized to be able to respond to member’s or user’s requests (Boyd and Ellison 2007; Raine and Wellman 2012; Ellison and Boyd 2013). For example, pattern 22 is synchronized with patterns 23, 24, 25, 26 and 30.

The SignaComm community (pattern 10) requires that all functionalities (pattern 11) execute their tasks to ensure the smooth running of the SNS. Pattern 12 (boundaries of SignaComm’s functionalities) establishes boundaries to each functionality, allowing it to perform its assigned tasks. It avoids the overlap with other functionalities.

Pattern 20 relates to user’s profile characteristics (Cardon 2008; Proulx 2012). The SignaComm community encourages the diversity of members in order to enrich its growth (Wellman 2002). Therefore, the growth of the SNS also depends on a well-balanced and represented community of members. This community would be able to support the interactions (pattern 30) between its members. For example, the interactions would help a member to solve a situation (Wellman 2002).

Pattern 20 has links with pattern 21 (members list). The latter pattern specifies the SignaComm target audience (e.g. the bidirectional relationships to define a reciprocated link between two members) (Bouraga et al. 2014). Members would belong to different cultures and social classes, as well as different age groups (Wellman 2002).

Pattern 22 (automatic translation) is a central functionality as it receives members’ translation requests from the SNS’s interface (pattern 100) and facilitates the communication between SignaComm members (pattern 21). Pattern 22 relies on the signage and signagrams, natural languages and linguistic rules, ontology, as well as dictionary to translate members’ requests (pattern 30).

Pattern 30 (activities) is mostly linked to patterns 20, 21, 22, 31, 40 and 100 to create nodes of activities, thus allowing members or groups to engage in various ways (Raine and Wellman 2012). These activities may include invitations to join the SignaComm, instant messaging and geolocation of members with their approval. Here, members have the opportunity to make acquaintances and connections, as well as to chat with members and groups of their choice. Depending on the proximity of members, some ties are strong while others are weak (Granovetter 1983; Ellison and Boyd 2013). These activities are displayed in the SNS’s interface (pattern 100), and generated data are stored in the secured database (pattern 40).

Pattern 31 (privacy) is mainly for patterns 2, 20, 21, 22, 30 and 40. This pattern allows every member to set their data sharing options with the IT administration, members and SNS environment (Gross and Acquisti 2005; Cardon 2008; Kennedy and Millard 2016; Proulx 2012; Bouraga et al. 2014; Waheed et al. 2017). It takes every member’s wish to accept or forbid the sharing or storage of personal information into consideration. We provide the following example: a member chooses not to publicly display their profile and then not to share their geographical position with the SignaComm and its environment. Pattern 10 (SignaComm community) must respect every member’s choice (Kennedy and Millard 2016).

Pattern 40 (IT administration) is connected to patterns 11 (SignaComm functionalities) and 100 (interface). To make the interface and functionalities real, it is necessary to set up an IT administration. The latter manages the database and security, modifies the SNS, analyzes the generated information and answers to members’ requests.

Pattern 50 (network of links and ties) creates and manages the network of relationships between all the patterns (Boyd and Ellison 2007; Raine and Wellman 2012; Ellison and Boyd 2013). It allows the information to circulate instantly and correctly in the SignaComm community.

Pattern 100 (interface) gives an overview of the SignaComm interface, with an emphasis on the space of exchange between SignaComm members. The SNS’ functionalities and IT administration contribute to its design (Morin et al. 2012; Jain et al. 2013). It includes the universal design principles (Spiliotopoulos et al. 2013; Chandler 2017).

Pattern 101 (pages) is the continuation of pattern 100. The SignaComm’s architecture includes the design of web pages and signagram buttons (Kordahi 2016). The SignaComm is created with a reduced number of pages, such as the registration, members and chat room pages. This design is followed to quickly access information, provide flexibility in use and initiate intuitive interactions (Jain et al. 2013; Spiliotopoulos et al. 2013; Kordahi 2016; Chandler 2017). The navigation between these pages is done through the signagram buttons.

Figure 5.2 shows an overall view of the SignaComm’s pattern. It includes the 14 patterns. We show the main links between the patterns to simplify the diagram’s representation.

5.4.1. Automatic translation

We analyze the situation where a SignaComm member uses the application to translate a sequence of syntagms (or text phrases) into a series of signagrams in real time, and engage in an informative conversation with a member or group of members. We present information to be quickly understood by members, to prevent some manipulation of personal data without their knowledge or permission and regardless of the computing device used (Kennedy and Millard 2016; National Commission on Informatics and Liberty 2020) (e.g. the portability of data).

The machine translation prototype and its results are constantly assessed according to the knowledge produced. The latter is acquired during the machine translation process and while fulfilling members’ requests. Following the automatic translation of key phrases into signagrams, we wish to know how members have understood the translated messages and which translations are useful to them (Suojanen et al. 2014). In other words, a member, who has viewed the translation result, has the choice of editing it. This option allows them to choose the translation which meets their expectations. It is also an invitation to contribute to the machine translation process, in order to provide more precision to the requests made. The member can then confirm their choice or restart the translation request (see Figure 5.3).

While developing this machine translation prototype, we face one main difficulty, namely the non-figurative legal corpus. The suggested solutions are, on the one hand, to segment and analyze a thematic text and, on the other hand, to only translate the syntagms related to the case (Kordahi 2015; Kordahi and Baltz 2015).

Here, for this machine translation, the expressions’ exactness is necessary to be able to break down their relations with other encompassing units. This would help by decreasing blunders and uncleanness in the translation process (Bar-Hillel 2003; McShane et al. 2005; Kordahi 2015; Kordahi and Baltz 2015). Consequently, we use the National Commission for Informatics and Liberty portal’s thematic text that presents reliable and relevant information.

Our model is composed of the ontology for the protection of personal data (Palmirani et al. 2018a, 2018b), the construction of a dictionary of signagrams also related to the protection of personal data (Takasaki 2006; Holtz et al. 2010; Kordahi 2013b) and the adaptation of the function translating text phrases into signagrams (Emel et al. 2000; Seme 2003; Kordahi 2015; Kordahi and Baltz 2015). We use the Natural Language Toolkit in Python (Bird et al. 2009). It allows connections with the ontology and dictionary, in addition to other functionalities (e.g. text processing, semantic reasoning).

We are particularly interested in the works of Palmirani et al. (2018a, 2018b), as their ontology is based on the application of the general data protection regulation. The accuracy, flexibility and reliability of this ontology are well in line with our work objective. Therefore, it is appropriate to integrate it in the project.

Figure 5.3 shows an example of the automatic translation of text phrases into signagrams. The user has the option to edit the translation result, where other signagrams will be shown.

5.4.2. Dictionary of signagrams

To our knowledge, published works related to the dictionary of signagrams for the protection of personal data are limited. We rely on the works of Takasaki (2006) and Kordahi (2013b) to design and develop this first dictionary, which is specialized. It provides information on signagrams to improve their understanding by any user.

The dictionary’s design is based on the correspondence of vector signagrams to homologous semantic-based concepts. We program a mapping between two resources. The first semiotic graphical resource contains signagrams’ external shapes (including the contours) and internal shapes (Kordahi 2013a). The external and internal shapes, coming from that graphical resource, are stored in the dictionary. The second resource is a semantic lexical one (e.g. the WordNet (Miller 1998)). The latter contains the concepts with their definitions and synonyms in English. The words, definitions and synonyms, coming from that lexical resource, are contained inside the dictionary (see Figure 5.3).

We create 50 signagrams based on the works of the United Nations Economic Commission for Europe (2006), Holtz et al. (2010) and Rossi and Palmirani (2019), as well as the “Fotolia” international image bank. The latter holds a large collection of images and symbols used globally. The signagrams’ colors and shapes follow the international charter road signs (UNECE 2006).

5.5.1. Interface pattern

The SignaComm’s interface is used to display two sorts of information: the resulting information from an exchange between SNS members and interactions between the SNS system and its members. We provide the following examples, which include sending and receiving instant messages, displaying automatic translation of written texts into a sequence of signagrams and viewing a member’s profile (see section 5.3, Figure 5.2).

The graphical user-interface is made up of a set of HTML web pages. It consists of a main interface and secondary one. The main interface is used to display the web pages’ content. The secondary interface is the navigation bar. It enables the browsing between the various pages (see section 5.3, Figure 5.4).

5.5.2. User profile pattern

We strive to respond to the ethical principles of SNS (Kennedy and Millard 2016; Ellison and Boyd 2013). We mainly focus on the member’s professional information (e.g. the profession, name and surname, profile display mode (private or public)). We respect information integrity and analyze the context in which it was saved in the SignaComm database. This information must not be distributed to third parties, before obtaining the owner’s consent (Kennedy and Millard 2016).

The user profile pattern performs three essential tasks. These are the registration of a user, invitation of a user and geolocation of members (see section 5.3, Figure 5.2). The first task allows a user to register and login to the SignaComm, which is a condition to use this SNS. The registration is done by submitting a user-name and password, as well as some information regarding the user (e.g. choosing to share their information (Cardon 2008; Morin et al. 2012; Proulx 2012) and geographical position with the SNS) (see Figure 5.4). The sign-in is done by submitting the member’s user-name and previously saved password. The second function allows a SignaComm member to invite another user by sending an electronic invitation (e.g. instant message) while using the other patterns (e.g. pattern 21). Pattern 20 is connected to a geolocation process to make it possible to perform the third task. The latter task automatically suggests a language of conversation (Yang and Lin 2010).

This pattern comprises an application page and a PHP function. The HTML application page collects the user’s registration information, including the name, physical address, email and address. The collected information is sent to the PHP function.

5.5.3. Machine translation pattern

We rely on our works developed in sections 5.3 and 5.4 to implement the machine translation in the SignaComm structure. Once implemented in the SignaComm, the translation pattern runs three consecutive tasks that are stored in this SNS database. The chat room page (written in HTML format) can receive the member’s input text. A first request transmits the input text to be automatically translated into vector signagrams. A second request displays the machine translation result in the same HTML page. And a third request waits for the member’s action to send the translated message to the activity pattern, edit the translation results or reset the automatic translation process (Seme 2003) (see Figure 5.4).

Figure 5.4 shows an example of the SignaComm and the translation results. Here, the reported digital identities are simulated using fake profiles. Member 1 writes an input text (we wish to transfer files and request advice), activates its automatic translation and then sends the resulting translation to a corresponding member 2. Member 2 replies to member 1 by writing, translating and sending a message. The signagrams’ reading direction is from left to right and top to bottom (Neurath 1974). The result of Figure 5.4 is comparable to Figure 5.3.

5.5.4. Activity pattern

The pattern of activities performs two simultaneous and programmed tasks that are saved in the SignaComm. Chat histories are saved in the database’s tables (see section 5.3, Figure 5.2). Through the server, the translation pattern receives requests from a member in the form of packets compliant with a common Internet protocol (e.g. the HyperText Transfer Protocol (HTTP) POST packets). These packets contain the translated information (the message is translated before delivery). The second task displays the instant messaging exchange between members on the chat room page (Yang and Lin 2010) (see Figure 5.4).

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