Definición de conceptos

The qualitative analysis with ViewS Microscope is illustrated with a sample of 26 YouTube videos related to how to prepare for a job interview. ViewS

Microscope was ran for different user profile groupings as in the previous Section. For the construction of viewpoint focus models we used a semantic distance threshold 3 (edges).

(I) Findings - Grouping by Age. We selected two age groups to compare

the viewpoint focus models. The first group (referred to as young) included 102 users with age between 18 and 23, based on the assumption that this is a period in their lives in which they study and do not have much experience in interviewing. The second group (referred to as older) included 109 users with age between 28 and 33, based on the assumption that during this period a person will be working and will have at least one job interview experience.

Mental-state. Figure 7.24 shows the contrastive semantic map for the

mental-state branch (WNAffect taxonomy of emotions) together with the associated viewpoint focus models (lattices). The older group's focus included more elements (43 for young and 61 for older) in the structure as well as more main focus elements (9 for young and 12 for older). This shows that for the given data set a larger breadth of emotion related entities was extracted from comments of older YouTube users. As Figure 25a depicts, the young group's focus is included in the older group's viewpoint focus particularly around ambiguous -emotion. Around the region of negative emotions, the young group's viewpoint focus is either included or overlapping with the older group's. Figure 25b depicts two overlapping regions, showing the dominating coverage of the older group's focus. In the region of positive emotions, the two foci mostly overlap (an example is shown in Figure 25c).

young

older

Figure 7.24 Contrastive semantic map of mental states(right) and focus

(a) young group‟s focus element included in older group‟s focus in the region of ambiguous emotion

(b) older group‟s focus covered a wider region of negative emotions

(c)only weak overlap was observed in the region of positive emotion. Exclusive ontology entities were extracted by comments of each group.

Young Older Common

Figure 7.25 Comparison of focus models between young and older user

groups.

Body language signal meaning. Figure 7.26 shows the contrastive

semantic map for the body language signal meaning branch (body language ontology) together with the associated viewpoint focus models (lattices). The

older group's focus included more elements (195 for young and 348 for older) in the structure as well as more main focus elements (5 for young and 7 for older). Moreover, the older group's focus model is structured in more layers than the young group's (17 for young and 19 for older). The above observations show that the older group's viewpoint is covering a larger breadth f entities and includes more implications, thus more composite than the young group's respectively. Although both focus models overlap, particular differences are observed around the regions of social interactions and normative attributes (Figure 7.27). This shows that for the given data set a larger breadth of emotion related entities was extracted from comments of older YouTube users.

young

older

Figure 7.26 Contrastive semantic map of body language signal

Young Older Common

Figure 7.27 Overlap of focus models of young and older users.

Regions of ontology entities related to social interaction and normative attributes were extracted only by older users.

(II) Findings - Grouping by Gender. For the comparison of viewpoints

based on gender, a random sample of male user profiles was selected to balance the with the female user profiles (105 users). A theoretical foundation for such a comparison includes that social signals (emotion and emotion expression) can be diverse between genders in particular contexts of interactions (e.g. job interviews)[161].

Mental-state. Figure 7.28 shows the contrastive semantic map for the

mental-state branch (WNAffect taxonomy of emotions) together with the associated viewpoint focus models (lattices). The male group's focus included more elements (71 for male and 51 for female) in the structure as well as more main focus elements (15 for male and 12 for female). This shows that for the given data set a larger breadth of emotion related entities was extracted from comments of male YouTube users. Many singular entity regions of the male user's focus are disconnected from the female user's focus including cruelty, identification, wonder and mood. In the region of positive emotion, the viewpoints are very overlapping (Figure 29a). However, in the negative emotion, although overlapping, the male user's viewpoint focus includes elements around sadness (Figure 29b) and annoyance (Figure 29c) which are missing from the female user's viewpoint focus.

male

female

Figure 7.28 Contrastive semantic map of mental states(right) and focus

(a) in the region of positive emotion the focus models mostly overlap between the two user groups

(b) a focus region of ontology entities related to sadness was extracted only by the comments of male users

(c) in the region of negative emotion the focus of male users included the focus of female users. A sub-region of ontology entities related to annoyance was extracted only from male users.

Male Female Common

Figure 7.29 Comparison of focus models between male and female user

groups.

Body language signal meaning. Figure 7.30 shows the contrastive

semantic map for the body language signal meaning branch (body language ontology) together with the associated viewpoint focus models (lattices). The

male user's focus included more elements (336 for male and 260 for female) in the structure. Although both focus models had the same number of main focus elements (6 in the second top layer), the male user's focus model is structured in more layers than the female group's (17 for male and 19 for female). The above observations show that the male group's viewpoint is slightly broader and more composite. Although both focus models overlap, particular differences are observed around the regions of normative attributes and psychological attributes (Figure 7.31). This shows that for the given data set a larger breadth of related entities was extracted from comments of male YouTube users.

male

female

Figure 7.30 Contrastive semantic map of body language signal

Male Female Common

Figure 7.31 Overlap of focus models of male and female users.

Regions of ontology entities related to psychological and normative attributes were extracted only by male users.

(III) Findings - Grouping by Location. For the comparison of viewpoints

based on location, a random sample of US user profiles was selected to balance with the GB YouTube users (36 users). similarly to the gender- social signals comparison, culture can also be a co-variant in peoples emotional experience according to particular contexts[161-163].

Mental-state. Figure 7.32 shows the contrastive semantic map for the

mental-state branch (WNAffect taxonomy of emotions) together with the associated viewpoint focus models (lattices). The US group's focus included more elements (34 for US and 24 for GB) in the structure as well as more main focus elements (9 for US and 8 for female) and layers (7 for US and 5 for GB). This shows that for the given data set a larger breadth of emotion related entities was extracted from comments of US YouTube users. However, there is a region related to annoyance in negative emotions from which ontology entities were extracted from users in GB and missed from US (Figure 7.33a). Regions of ontology entities related to negative fear and sadness were not extracted by comments of users in GB (Figure 7.33b and c). In the region of positive emotion, the ontology entities extracted from comments of both groups of users were sparse.

US

GB

Figure 7.32 Contrastive semantic map of mental states(right) and focus

(a) in the region of negative emotion, a focus region from the GB users‟ focus model included the focus of US users. A region of entities related to annoyance was exclusively extracted from comments provided by users in GB.

(b) a region of entities related to negative-fear was extracted in the focus model of US users, illustrated with the two overlapping focus elements.

(c) a region of entities related to sadness was extracted in the focus model of US users, illustrated with the two overlapping focus elements.

US GB Common

Figure 7.33 Comparison of focus models between US and GB user groups.

Body language signal meaning. Figure 7.34 shows the contrastive

semantic map for the body language signal meaning branch (body language ontology) together with the associated viewpoint focus models (lattices). The

GB users' focus included more elements (88 for US and 100 for GB) in the structure. However, the focus model extracted from US users had more main focus elements (6 for US and 4 for GB) and the structure had more layers (13 for US and 11 for GB). Although both focus models had the same number of main focus elements (6 in the second top layer), the male user's focus model is structured in more layers than the female group's (17 for male and 19 for female). The above observations show that the male group's viewpoint is slightly broader and more composite. Although both focus models overlap, a particular difference is observed at the linguistic communication region of the ontology branch, where ontology entities extracted from GB user's comments were missed in US users' comments and reversely. Most overlap is observed in the region of psychological processes. Figure 7.35 depicts these observations.

US

GB

Figure 7.34 Contrastive semantic map of body language signal

US GB Common

Figure 7.35 Overlap of focus models of US and GB users.

Regions of ontology entities related to linguistic communication were exclusively extracted from focus models of each user group. Strong overlap is observed in the region of entities related to psychological processes.

Using ViewS to extract and compare viewpoints in a small sample data set made possible to examine the similarities and differences of the viewpoints between different user groups. To explore diversity, domain aspects where viewpoints are similar or different were identified to better understand the users.

7.4 Discussion

In this Chapter ViewS Microscope was instantiated for two different types of user generated content: content collected in a closed social space, to illustrate the support of for the viewpoints focus representation requirements; and, content collected from a Social Media platform – YouTube, to illustrate the analytical utility for larger volumes of data. The application of Views is highly dependent on the data/content collection, from which particular implications have to be considered with respect to the methodology including required content features, purpose, application and quality.

Using closed social space. Content collection from closed social space

allows for controlled elicitation of use generated content. Similarly to empirical evaluation methods for user modelling [164], the experiments/analysts can identify and elicit the features of user generated content that he/she is interested in. Guidelines are explicitly given to users, based on the purpose of the study, of which the users are aware off. The data is expected to be of higher quality, with reduced noise levels, however, achieving large volumes can be proved time consuming and resource expensive. How easy it is to avoid bias in the user model, therefore to increase authenticity[164]; To overcome this, open social spaces can be exploited, which however has its own disadvantages as a trade-off.

Using Social Media. Social Web provides an abundance of authentic user

generated content. Making sense of its users has been proved beneficial and challenging in recent research streams, including user modelling with semantic web technologies [165]. However, Social Web does not provide the facility to collect all the possible desired features of user generated content, as the experimenters/analysts do not have direct control on the user interfaces for interaction.

In this work we acknowledge the benefit that can be produced with a synergy of semantic web technologies and machine learning or computational linguistic approaches, e.g. for topic detection. For example, given a user‟s comment on job interview related video in YouTube, to whom does the comment refer: the interviewer or the applicant different viewpoint focus models can be constructed and analysed with intelligent text summarisation techniques [166].

Moreover, researchers have to deal with the noisy content that social media platforms are characterised by, especially when involving semantic annotation[98, 167]. The application of the extracted user models as well, has to be carefully monitored by domain experts; while in closed social spaces, the domain expert him/her self can control the content elicitation mechanism from the beginning of the study.

With ViewS Microscope, we demonstrated a semantic approach for user viewpoint modelling. It comprises an analytical tool for user generated content to support experimenters and analysts at the design stage of a system. ViewS can be extended in several directions. One of them, considering the large volumes of data especially concentrated in Social Web, concerns the frequency of annotations of particular ontology entities. As discussed in Section 6.7 regarding the focus model construction (and

comparison of models), more frequent entities in the data set should be given more weight for analysis.

Moreover, one has to consider the distribution of user generated content with respect to different user profiles and digital objects. In the Social Web, as opposed to close-controlled social spaces, it can be found extremely difficult to balance the selected user profiles with the content contributed to digital objects. For example, exploiting YouTube, it is difficult to trace users that have contributed to several related videos. Although we acknowledge that no conclusive observations are aimed to be made (e.g. for stereotyping), the application of ViewS as an analytical tool, guided by a domain expert‟s input, can be proved useful for getting insights. Digital objects that complement viewpoint focus models can be suggested to users within a user group that is currently analysed. Similarly, focus models can be suggested to users within a group when they have interacted with the digital objects. This utility can be included by a domain expert in an adaptive system to expand and broaden the users‟ viewpoint (suggesting other digital objects) on one hand, and on the other hand to increase their awareness (using the same digital objects) respectively.

Chapter 8