Semantic Aggregation and Zooming of User Viewpoints in Social ...

Semantic Aggregation and Zooming of User Viewpoints in Social Media Content Dimoklis Despotakis, Vania Dimitrova, Lydia Lau and Dhavalkumar Thakker University of Leeds, School of Computing, Leeds, LS2 9JT, United Kingdom {D.Despotakis, V.G.Dimitrova, L.M.S.Lau, D.Thakker} @ leeds.ac.uk

Abstract. Social web provides rich content for gaining an understanding about the users which can empower adaptation. There is a current trend to extract user profiles from social media content using semantic augmentation and linking to domain ontologies. The paper shows a further step in this research strand, exploiting semantics to get a deeper understanding about the users by extracting the domain regions where the users focus, which are defined as viewpoints. The paper outlines a formal framework for extracting viewpoints from semantic tags associated with user comments. This enables zooming into the viewpoints at different aggregation layers, as well as comparing users on the basis of the areas where they focus. The framework is applied on YouTube content, illustrating an insight into emotions users refer to in their comments on job interview videos. Keywords: Social media content, User model representation and extraction, Viewpoints, YouTube, Adaptive learning

1

Introduction

The Social Web offers an abundance of user-generated content. It consists of uploaded resources and exchange of statements from readers and creators of these resources (e.g. in YouTube). For the user modelling community, this provides a rich source for extracting some knowledge-nuggets about the users. A better understanding of users will help improve subsequent adaptation and personalization. There is a recent trend in exploiting Semantic Web technologies for understanding user behaviour in social media [1, 2]. Semantics offers a great potential for modelling users by providing structure, facilitating organisation and aggregation of user contributions. This enables exploration of the large volume of user-generated content and the breath of opinions expressed by users providing opportunities to build on diversity [3] [4]. A common approach for user modelling from social media is to extract user characteristics based on concept/term lists linked to an ontology. This paper argues that semantics offers a greater potential for user modelling by providing an explicit structure to position a user model within the domain. This can allow discovering similarity, complementarity and overlap in user models to enrich the personalised experiences.

adfa, p. 1, 2011. © Springer-Verlag Berlin Heidelberg 2011

This paper proposes a framework for exploiting semantics to get a deeper understanding about the users by extracting the domain regions where the users focus. These focal areas are defined as viewpoints. The paper outlines a formal framework for extracting viewpoints from semantic tags associated with user comments. The framework is reported in (Section 3). The application of the framework is illustrated by a case study (section 4) on the detection of user viewpoints on social signals (emotion and body language) and the diversity of the viewpoints in a set of videos related to job interview.

2

Related Work

This work contributes to recent research on modelling users from Social Web, e.g. to interpret social web interactions [5] or address the cold-start problem in recommender systems [1]. Semantic Web technologies are being increasingly adopted as a powerful mechanism to make sense, aggregate and organise Social Web content by linking it with ontology concepts [2]. The extracted user profile is in the form of a set of concepts from one or several ontologies, which can enable inferring additional knowledge about the user [6]. We build on this base line and use semantic augmentation linking user comments to concepts in an ontology that presents the domain of interest. Our significant contribution to user modeling from Social Web is the further semantic analysis we conduct over the ontology concepts to aggregate them into regions that show where a user (or a group of users) is focusing on, representing this formally as viewpoints. Furthermore, we provide a zooming mechanism to analyse viewpoints at different granularity levels for concept aggregation. The notion of user viewpoints is related to perspectives and opinions. The PerspectiveSpace presented in [7] builds upon a quantitative representation of beliefs about items, exemplified with a recommender system that exploits belief-based similarity of items (measuring user agreement and disagreement). Opinion mining research (c.f. [8, 9]) provides quantitative models where viewpoints are compared using polarity (positive to negative) or classified based on category assignment scales. Similarly, diversity of opinions in text is analysed in a quantitative way [10, 11]. User diversity is examined in [12] based on folksonomies derived from social annotations and conducting quantitative analysis of user tags. All these approaches lack qualitative analysis of content that can show user positions within the domain of interest, and can be used to explain the diversity in user generated content. The approach presented here provides such qualitative analysis, exploiting semantic aggregation and zooming. Our qualitative modelling approach for deriving viewpoints is based on Formal Concept Analysis (FCA) [13] over an ontology concept space. FCA has been utilised for ontology navigation [14], for user profiling over document tags [15], and for deriving personal ontologies over document structuring with tags [16]. Uniquely, the FCA adaptation shown in this paper exploits an ontology that represents the domain, and defines parameters for extracting user viewpoints and related focus spaces.

3

User Viewpoints Modelling

We present here a framework for capturing, representing and analyzing user Viewpoint Semantics (ViewS) from Social media content. Preliminaries. ViewS takes as an input user statements from a set of social media digital objects which include user-generated textual content related to a specific topic (e.g. user comments on YouTube videos related to job interview, a blog with user stories about travelling, or a collection of Tweets about a conference). The specific topic is linked to a domain ontology (or several ontologies); each ontology corresponds to some dimension about the topic which is of interest for further investigation (e.g. WordNet-Affect1 can be used to analyse what emotions people mention when watching YouTube videos on job interviews, Geonames2 can be used to analyse the locations people mention in travelling blogs, or the Conference Ontology3 can be used to analyse what conference concepts are being discussed in tweets). We assume that the domain ontology is represented in RDF or OWL, which ensures that ViewS complies with existing standards and can exploit existing ontologies and available ontological tools. Semantic augmentation is conducted over the user-generated content, linking textual input to entities in the chosen domain ontologies. The output of semantic augmentation – a set of ontology entities - is used to analyse the focus of attention of the users, which is represented as viewpoints (as defined below). Definition 1: User Viewpoint. Consider a set of users ( , where can present an individual user or a group of users) who have made statements in a set of digital objects ( ) related to the same domain, represented with an ontology . After some semantic augmentation, the user statements are linked to a set of ontology entities ( ). Using these notations, the viewpoint of user(s) is defined as a tuple , where is the viewpoint focus space which is a layered overview onto the hierarchy graph of the ontology (see Definition 5 below). Following the above definition, the key challenge for extracting viewpoints is how to extract the viewpoint focus space from the set of ontology entities which is overlaid upon the ontology . This requires a mechanism for deriving sub-graphs in which show regions of the domain where the set of users are focusing on, i.e. there is an aggregation of ontology entities from in close semantic proximity. As a first step for extracting the viewpoint focus space we need an algorithm for calculating the semantic distance between two ontology entities. Semantic Distance between Ontology Entities. To calculate the distance between two ontology entities, we exploit the subsumption (rdfs:subClassOf) and membership (rdf:type) relationships in ; the algorithm is presented in Fig. 1.

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http://wndomains.fbk.eu/wnaffect.html http://www.geonames.org/ 3 http://data.semanticweb.org/ns/swc/swc_2009-05-09.html 2

calculate_distance( // input is an ontology and two ontology entities and if or or : // the two entities are the same or one of the entities is an instance of the other return 0; // the distance between an instance and its parent is assigned to 0 else: //get all paths from to the top concept in the ontology //get all paths from to the top concept in the ontology //get the intersection of both sets of paths which provides an ordered set of common parents to the top if return else: return getPathToOWLThing( if return ; else: return

: //get the first element from the ordered set of common parents and check if it is the top ;// both entities are connected via the top concept, so belong to two different ontology branches, i.e. are too far ; //distance is the number of concepts in the path between the two entities //going through their most specific common parent : // a recursive algorithm to get the path from an entity

to the top element in the ontology

:

; //get the super-class or type οf

in

Fig. 1. The algorithm to calculate semantic distance between two ontology entities. The algorithm calculates the shortest path between ontology entities based on the first common parent in the ontology hierarchy (subsumption and membership relationships).

Aggregation mechanism. The semantic distance provides a crucial ingredient for deriving aggregated areas within the ontology space but is not sufficient to derive viewpoint focus space – we need an appropriate aggregation mechanism which utilises the computation of semantic distance. There are two crucial requirements for such a mechanism: (a) it has to allow grouping of entities into some abstract conceptual structures and (b) it has to deal with changing the granularity of the aggregation to allow analysis of viewpoints at coarse-grain or finer-grain over the domain. To address the above requirements, we will define a qualitative framework for deriving the viewpoint focus space which adopts the Formal Concept Analysis (FCA)[13]. FCA provides a formally defined way to extract abstract conceptual structures from a collection of objects and their attributes. This adoption is based on the FCA main components, as follows: ─ formal context, which assigns attributes to objects; ─ formal concepts, which are the derived collections of objects and attributes through the knowledge context; ─ concept lattice, which defines a conceptual knowledge structure with hierarchical relationships between the derived knowledge concepts. The above points are defined below, utilising also the semantic distance algorithm. Definition 2: Formal Context  Viewpoint Context. The viewpoint context is a triple where   (i.e. the viewpoint’s set of ontology entities is used as both objects and attributes for FCA; we preserve distinct annotations to follow the FCA definitions). is read as: is related to , where is the semantic distance, calculated with the algorithm presented in Fig 1., while is a threshold for assigning the binary function based on the semantic distance between objects and attributes in (i.e. ontology entities )and is calculated with equation (1).

(1),

where is an index distance value of all possible distances between annotated ontology entities in and is the frequency of it. is lower capped to 1. Definition 3: Formal Concept  Viewpoint Element. Consider a viewpoint context and let and . The pair is called a viewpoint element : is the set of ontology entities related to all the ontology entities belonging to , and is the set of ontology entities of which the related ontology entities belong to , i.e. . and represent the extent and the intent of the viewpoint element. forms a cluster of annotated ontology entities formed based on the application of I, i.e. these are ontology entities which are close given the semantic distance and the threshold. The set of viewpoint elements is denoted as . Definition 4: Viewpoint Focus Element. Let be a viewpoint element. The set of object entities constitutes the boundary of a region in . Using , we define the viewpoint focus element as the ontology sub-graph representing the product of the aggregation of all possible paths between all elements of over . Definition 5: Viewpoint Focus Space. The viewpoint focus space is defined as the union of all the viewpoint focus elements extracted by the viewpoint elements in the viewpoint context: Definition 6: Concept Lattice  Viewpoint Lattice. Given a viewpoint context , let and , two viewpoint elements. An inheritance relation is defined as with be a sub-concept of (and vice versa, super-concept) according to the condition: . Then ( ) is a complete lattice, called viewpoint lattice, denoting viewpoint element relations. The viewpoint lattice provides a structure to explore the viewpoint focus space. This structure is a composition of aggregates of ontology entities and enables semantic zooming into different granularity for aggregation. The lowest level of the lattice includes a list of ontology entities without any grouping. The highest level in the lattice gives the aggregated focus region which a user (or a group of users) has focused on. Going up in the lattice, we are exploring a more abstract space. Hence, following upward links in the lattice allows zooming into the viewpoint focus space, i.e. we get larger aggregates of ontology entities. In the following section, we will illustrate an exploration through the focus space of viewpoints derived from YouTube content. Implementation 4 : ViewS-Microscope. The ViewS framework has been implemented in Java. The semantic augmentation utilises the Stanford Parser for text processing, as well as linguistic and semantic resources for semantic enrichment and tagging. This is presented in more detail in [17]. For ontology processing, we use the 4

The views software is accessible at http://imash.leeds.ac.uk/services/ViewS

Jena Semantic Web framework5 to retrieve the ontology(ies) structure, which is then transformed to a tree-graph representation with vertices ontology entities (concepts and instances) and edges the rdfs:subClassOf and rdf:type relationships. The formal mechanism presented in section 3 is implemented A visualisation software is implemented to illustrate the outputs of the formal framework presented in Section 3. This allows to load an ontology and semantically augmented corpus of user-generated content linked to this ontology. Using the ontology entities from the semantic tagging, the corresponding viewpoint context, elements, and lattice are constructed following the above definitions. Furthermore, two sets of ontology entities over the same ontology representing different users or groups of uses can be loaded and compared using their aggregated viewpoint focus spaces. The viewpoint point focus space is explored through the lattice by zooming into different granularity levels of aggregation. This resembles microscope-like exploration of viewpoints.

4

Examining YouTube Content with ViewS-Microscope

This section will illustrate the benefits of the formal mechanism for semantic aggregation and zooming presented above, by using the viewpoint visualization software ViewS-Microscope to explore the extracted viewpoints. An experimental study was conducted within one of the use cases of the EU ImREAL 6 project. ImREAL examines social signals in interpersonal communication to enrich simulated environments for learning. Job interview was selected as an interpersonal communication scenario where emotion is an important dimension. YouTube provides a rich corpus of user interviews, where user comments can provide useful insight about emotions people associate with job interview situations. Querying the YouTube API, a corpus of 600 videos with 10,656 comments from 8,083 users was collected. 7 The YouTube API queries were constructed based on keywords elicited during a study for the evaluation of an activity modelling ontology – AMOn - including job interviews8. The videos were filtered based on the video metadata for video category (including “Education”, “Non-profit and Activism”, “People and Blogging” and “How-to and Style”) and video tags (e.g. “job” and “interview”). For semantic augmentation of the textual comments WordNet-Affect was utilised to represent a taxonomy of emotions. The configuration also included selection of WordNet lexical categories (e.g. noun.communication, verb.emotion) and SUMO concepts (e.g. Answering, SubjectiveAssessmentAttribute), relevant to interpersonal communication and social signals9 for

5 6 7

8 9

Jena Semantic Web framework at: http://jena.apache.org/ http://www.imreal-project.eu/ The data and annotations are available at: http://imash.leeds.ac.uk/services/ViewS/Data/YouTubeDataAnnotated_JI.xml ImREAL EU Project, deliverable D7.3: http://www.imreal-project.eu/ The selection was performed by a social scientist expert in manual content annotation for activity modelling.

sense detection during the semantic enrichment process. ViewS was run to extract viewpoints, and ViewS-Microscope was utilised for exploring the focus spaces. 4.1

Semantic Aggregation and Zooming of Group Viewpoints

We will illustrate the application of ViewS over the complete set of comments, selecting age10 as the characteristic for user grouping and comparison. Based on the indicated age in the YouTube profiles, the data sets (min:13 max:85 median: 26 mean: 20.09 sd: 9.63) was divided into six groups: [13-18], [19-21], [22-26], [27-36], [3754] and [55-85] according to the normal distribution. Quantitative analysis over the tagged corpus showed that as age increased, concepts related to emotion were more frequently mentioned (Spearman’s rho = 0.94, p = 0.034), and more distinct ontology entities were extracted (however no significant correlation was detected for this). Based on this, it could be assumed that older users mentioned a larger breadth of emotion entities11. However, quantitative analysis could not give an insight into the differences.

Fig. 2. Partial structure of the viewpoint lattice in the study. * indicates partial view of a branch

To illustrate the benefit of ViewS for comparing user groups, we select the groups of the youngest and oldest users. With ViewSMicroscope we can examine user viewpoints at different granularity level. Ontology entities extracted from user statements are grouped in viewpoint focus elements (Definition 5), organised on a viewpoint lattice (Definition 6). ViewS-Microscope can zoom into different lattice layers enabling exploration of these elements based on the lattice hierarchy relationships (see Fig. 2). Each node in the example shows a viewpoint focus element.

The analysis starts with a viewpoint focus element in the first level (e.g. L1 in Fig. 2), which shows the smallest clusters of ontology entities. For both groups, the first level is made of two ontology entities anger and general emotion. After zooming into lower layer viewpoint elements linked to anger (e.g. L3 in Fig. 2), it is possible to see an expanded viewpoint focus space for both age groups (Fig. 3a and 3b). The difference between the groups is hardly noticeable.

10

11

The calculation are based only on those profiles for which age was provided and was smaller than 85 years old. This analysis aimed at gaining an insight of the data set possible trends, rather than concluding to decisions for stereotyping, for which deeper analysis and evaluation would be required in more domain.

However, when zooming further into L5 (which gives more inclusive level of aggregation with larger semantic distances between ontology elements), it is possible to see more clearly some difference and similarity between the groups. For example, in Figure 3c, for the younger group, the area around three particular concepts - negativefear, anxiety and sadness (marked as area 1) is dense which indicates greater focus of this group around these three concepts. With a similar zooming (e.g. same level and viewpoint element) on the older group, a proportion of the concept space (e.g. marked as area 2) is only seen in this group (concepts: ingratitude, despair and hopelessness).

(3.a) Younger group L2 viewpoint focus element derived from anger at L1: anger,

(3.b) Older group L2 viewpoint focus element derived from anger at L1: anger,

anxiety, compassion, emotion, humility, sadness and shame, general-dislike

anxiety, compassion, emotion, humility, ingratitude, sadness and shame, generaldislike

(3.c) Younger group, L5 viewpoint focus element (derived from anger at L1)

(3.d.) Older group, L5 viewpoint focus element (derived from anger at L1)

Fig. 3. Zooming into the viewpoints of both groups with ViewS-Microscope starting from layer 1 viewpoint focus element - anger. Each dot indicates an entity from the ontology. Each highlighted/darken dot indicates an entity from the viewpoint by the particular group.

In a similar way, the focus space of positive emotions mentioned by each group is examined (Fig. 4). At L5, a slight diversity in the viewpoint focus elements is detected - compared to the older group, the younger group has more breadth in concepts related to general positive emotions.

(a) Younger group, L5 viewpoint focus element in the positive emotion branch

(a) Younger group, L5 viewpoint focus element in the positive emotion branch

Fig. 4. Zooming into the YouTube comments from both groups in the ViewS Microscope for the ontology branch on positive emotions. While the quantitative comparison indicates differences between the two user groups, it does not give an insight into what these differences are with respect to the domain. The comparison with ViewS-Microscope gives deeper insight into the areas of focus for these groups over WordNet-Affect, showing their differences. Zooming into different aggregation points facilitates exploration of the underlying reason. 4.2

Semantic Aggregation for Individual User’s Viewpoint Focus

The previous section illustrated the use of ViewS-Microscope for examining viewpoints of different groups which, by its nature, generally has higher number of statements and denser ontology entities. In this section, we will use ViewS-Microscope to examine viewpoints of individuals where sparse ontology entities are a norm. For this analysis, we selected four YouTube videos used within an exploratory search environment (called I-CAW[18]) developed as part of the ImREAL project. ICAW is a semantic data browser that aggregates digital traces, such as social media comments or personal stories, on a specific topic (e.g. Job Interview) for informal learning. The selected four YouTube videos resemble real job interview situations. ViewS-Microscope allows exploration of an individual user’s viewpoint, based on the semantic tags by that user – to illustrate this, we have chosen a female aged 22 from US. Her viewpoint focus element at L1 is plotted in Fig. 5a. The conceptual space is quite sparse and is around the ontology entity “anxiety”. Fig. 5b shows the closest group to this user (i.e. female users, aged 22-26, from the US). The group has mentioned a broader range of emotion concepts. It is also possible to see additional (complimentary and contrasting) ontology entities within the group viewpoint focus. Plotting the viewpoint of all female users (Fig. 5d) without restricting location, similar comparisons can be made. For example, overall the US female in dataset under study is utilising fewer types of positive and negative emotions, both depth and

breadth-wise, than the females all over the world combined. In a similar ways, we can make comparison between two genders. In the used dataset, male users have mentioned more emotion concepts (both depth and breadth-wise). More importantly, males have named several concepts under the neutral emotion branch that are missing in the group viewpoint of female users.

(a) Viewpoint focus element L1 of a user – female, age 22, US

(b) Viewpoint focus element L1 for a group of users – female, age 22-26, US

(c) Viewpoint focus element L1 for a group of users – females, age 22-26 any location

(d) Viewpoint focus element L1 for a group of users – males, age 22-26, any location

Fig. 5. Semantic aggregation for an individual user’s viewpoint focus – applied over a subset of four YouTube videos showing job interview examples.

5

Discussion and Conclusions

This paper presented a framework for capturing, representing and analysing user viewpoints from social media content. Central to this framework is the performing of qualitative analysis by exploiting semantics to get a deeper understanding about users. This contributes to user modelling research by providing a richer representation of the user, extracted from social media content - viewpoints can be seen as a qualitative representation of user models (groups or individuals). Crucially, our approach allows semantic aggregation and zooming into different granularity levels of the semantic

space which can empower user adaptation. We will illustrate this within a useradaptive learning domain, considering the application context of ImREAL. Augmented User Modelling. Augmented user modelling is about getting insights about users from social web to improve adaptation in traditional systems. People nowadays are leaving digital traces in terms of blogs, tweets, comments etc. on the Social Web, providing a sensor of user activities and experiences, which can be a valuable source for personalisation. An application that can benefit from augmented user modelling is a user-adaptive simulated environment for learning which adapts the content to user profiles. One of the known challenges for such adaptation is the cold start problem. Using ViewS, it is possible to create group profiles from social content by aggregating and representing various group viewpoints and focus spaces. Using ViewS viewpoints of groups (e.g. based on age) based on collective statements made on digital objects representing some activity (e.g. an activity in the simulator) can be derived. A new user of the simulated environment can be assumed to get similar viewpoints to a user group with the close demographics, i.e. the group viewpoints can be used in a stereotype-like way. If we a viewpoint of the user (e.g. she has made a comment and it is linked to domain concepts) ViewS can help with mapping of the individual user’s viewpoint with the group viewpoint and finding complementary and similarity with viewpoint elements (and subsequent statements). This can be utilised to perform adaptation and broaden the user’s perspective over the domain space. Adaptation Authoring. User-adaptive learning applications generally have a design phase where instructional designers plan scenarios, exploration paths and content to offer to users. Zooming through the viewpoint lattice over the focus space allows: (a) Path selection: the path scenario can be built over the viewpoint lattice given current situations represented by viewpoint and going from specific to more generic spaces, i.e. exploring broader aspects. As demonstrated in Section 4.1, a current situation can include a small number of entities and progressively, by following upward links, can expand the knowledge space based on the viewpoints structure. (b) Content presentation: different granularity aggregates of focus can be presented to users, e.g. of different expertise and awareness, and at a different progress stage. As demonstrated in Section 4.2, it is possible to analyse viewpoint focus of younger group and discover areas they concentrate on, areas they miss (for example, a particular category of emotion missed by this group). The instruction designer might decide to include a scenario and training content that include domain areas this group may be missing. Future Work. The implementation of the framework is dependent on three main parameters: (a) the definition of which can vary the viewpoint lattice and focus space composition, (b) the ontology structure represented by ontology features, e.g. density and expressivity, (c) definitions of lists of abstract concepts for partitioning the ontology, e.g. the concept emotion could be considered as abstract (the same as the top concept), and excluded from semantic distance paths. This will allow clustering at a lower level by separating positive and negative branches. We are implementing automatic functionalities to parse and query the viewpoint focus space based on the extracted lattice structure, and plan to examine the effect of different parameters.

Acknowledgments. The research leading to these results has received funding from the European Union Seventh Framework Programme (FP7/2007-2013) under grant agreement no ICT 257831 (ImREAL project).

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