User-Oriented Analysis of Interactions in Online Social Networks
Post on 12-Dec-2016
18 1541-1672/12/$31.00 2012 IEEE IEEE INTELLIGENT SYSTEMSPublished by the IEEE Computer Society
User-Oriented Analysis of Interactions in Online Social NetworksRubn Fuentes-Fernndez, Jorge J. Gmez-Sanz, and Juan Pavn, Universidad Complutense de Madrid
integrate people and
a way to describe
patterns of social
interests through recommendations, links, or documents; and build lists of people with whom they are connected. These mecha-nisms are not mere replicas of their real-life counterparts; instead, the scale and abil-ity to make information visible to all OSN members let people draw conclusions and make contacts that they otherwise couldnt.1 For example, people find friends that they lost contact with long ago and discover new interests through others having similar pro-files; researchers observe societal trends and get immediate feedback from their commu-nities; and software engineers gain access to large test populations for their innovations.
The information available from OSNs is heterogeneous and used for different kinds of analysis. Engineers study the logs of sup-port systemsfor example, the applications and webpages that members use to interact with the OSN, the server systems, and mon-itoring toolsto improve their design, find bugs, and provide digests with higher-level
information to other stakeholders. OSN managers study these digests for many rea-sons: to discern trends in communities, user preferences or conflicts, and desired system features, among others. Social researchers are interested in members motivation, orga-nization, rules, and economic aspects.
These studies tend to follow one of two main approaches. One approach addresses the need to analyze large amounts of data such as link graphs, usage time, or preferred features.2 These types of studies are for discovering gen-eral trends, connection structures, or group profiles. The significance of such data emerges from statistical distributions, and data-mining techniques are common in such cases.
The second approach addresses the need for analyses focused on specific interactions.3 These types of studies are useful, for in-stance, for customization, ethnographic and cognitive studies, interface design, and soft-ware maintenance. The significance of this data comes from the way individuals perform
Online social networks (OSNs) are among the most popular sites on the Internet, according to the Web analytics site Alexa (www.alexa.com). OSNs are essentially real-world networks adapted to the Web.1 Members con-
struct personal profiles with the information they want others to know; share
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certain actions. Interpretation usually demands intellectual endeavor and in-volves multidisciplinary teams, requir-ing skills from diverse areas such as software engineering, sociology, psy-chology, and economics.
Drawbacks and ProposalThe dichotomy between these two approaches has a negative impact on the study of OSNs.4 It is difficult to discover new behaviors without de-tailed and costly scrutiny. Analysts (for example, engineers, managers, or researchers) begin studying data sets; they interpret them and generate hy-potheses about what people are doing and why, then validate their hypoth-eses by gathering new information. This process can be performed for spe-cific situations but is not cost-effective
for large numbers of interactions (see the sidebar, Online Social Network Analysis and Its Limitations). For instance, finding errors in the design of OSN support systems requires en-gineers to continuously collect and distill logs of system activity for designers, who use them to check if their hypotheses are correct and pro-pose changes to the system.
We intend to bridge this gap with a framework that formalizes some as-pects of a user-oriented OSN anal-ysis as patterns. These patterns, or social properties, represent knowl-edge grounded in the social sciences about motivation, behavior, organi-zation, interaction, and learning. Pat-tern specification consists of a design description in a visual modeling lan-guage thats accessible for human and
automated processing, along with further explanations about its so-cial basis and meaning. The analysis looks for occurrences of pattern mod-els in the information available about the OSN, which must have previously been described in the same model-ing language. This information in-cludes the design of support systems, the logs of user activities, and con-tent. The pattern correspondences in the design, logs, and content provide a first filter and interpretation of the raw data to the analysts. This proce-dure reduces the need for intellectual analysis to only those situations re-quiring experts understanding. For instance, the first automated interpre-tation can highlight members show-ing little activity in the OSN, but ex-pert skills are required to determine
The most difficult part of studying online social net-works (OSNs) is discovering relevant groups of data and interpreting them in terms of people and systems features.1 Trying to partially automate this analysis, some studies set up models that become their theoretical basis. These studies mainly focus on link graphs and message exchange.
The analysis of link graphs considers that the topology of networks mirrors human concepts and relationships. These graphs are analyzed with metrics such as the indegree, centrality, and closeness,2 which are applied to explicit and implicit relationships. Examples of explicit relationships are those between contents (that is, hyperlinks in webpages), between users (such as acquaintances, friends, and groups), and between both (for example, participation in events and recommendations). From these explicit relationships, the analysis extracts implicit relationships such as inter-est groups, community leaders, or relevant contents for topics.3
Other studies try to acquire further insights on interac-tions through exchanged messages.4 Message contents de-scribe OSN topics and their evolution and can be considered a kind of implicit link. The difficulties of analyzing free text lead analysts to focusing on messages between users and systems. For instance, instead of analyzing chat between two users, analysts focus on requests to the system inter-face, their types, and the time stamp of the messages.
These studies show two main limitations regarding hu-man elements. First, the studies make assumptions that apply poorly to real situations. For instance, they assume perfect knowledge about interactions or unambiguous in-terpretations of messages. Second, the studies usually defer
all data interpretation to human experts, as processing is independent of the actual semantics of the observations. Enabling automated reasoning on these facts requires for-malizing knowledge from social sciences as well as formaliz-ing the observations. Efforts in this directionfor instance with ontologies and logicsface difficulties in capturing the informal and holistic perspectives of social studies.
The exploitation of contents is also limited, as most stud-ies rely basically on structured information, such as tags of posts, links, emails, or paper coauthorship.2,3 Going beyond this requires natural-language processing.5 In any case, the collaboration of social researchers is required to interpret results from a user and group perspectivefor instance, regarding the intended meaning of a communica-tion or its potential effect in the group dynamics.
References 1. J.A. Bargh and K.Y.A. McKenna, The Internet and Social Life,
Ann. Rev. Psychology, vol. 55, 2004, pp. 573590. 2. P. Mika, Flink: Semantic Web Technology for the Extraction
and Analysis of Social Networks, J. Web Semantics, vol. 3, no. 23, 2005, pp. 211223.
3. C. Wilson et al., User Interactions in Social Networks and Their Implications, Proc. 4th ACM European Conf. Computer Systems (EuroSys 09), ACM, 2009, pp. 205218.
4. M. Naaman, J. Boase, and C.H. Lai, Is It Really About Me? Message Content in Social Awareness Streams, Proc. ACM Conf. Computer Supported Cooperative Work (CSCW 10), ACM, 2010, pp. 189192.
5. A. Qamra, B. Tseng, and E.Y. Chang, Mining Blog Stories Us-ing Community-Based and Temporal Clustering, Proc. 15th ACM Intl Conf. Information and Knowledge Management (CIKM 06), ACM, 2006, pp. 5867.
online Social Network analysis and its limitations
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whether they have problems with the interface or they are not motivated by the available contents.
This approach needs a suitable theoretical framework to provide the concepts for the properties de-scription and potential patterns. For this purpose, we have adopted activ-ity theory (see the sidebar, Activity Theory). AT is a social-research par-adigm with a holistic approach to the study of societies, covering every-thing from large-scale social move-ments to individual cognition in the
framework of historical development. AT constitutes our primary source of knowledge and metaphors that can be adapted for the study of OSNs.
AT has been already applied in soft-ware design, as the sidebar mentions. We use three main results from previ-ous research. First, we use UML-AT, a Unified Modeling Language (UML) profile that formalizes the conceptual framework of AT. UML-AT is the basis for the description of social prop-erties as models. Second, we adapt some of the AT patterns identified
in other software development fields for the analysis of OSNs. Third, we apply the results of studies on inte-grating AT and the agent software paradigm to describe the systems supporting OSNs in terms of AT con-cepts.5 This latter description facili-tates the integrated analysis in OSNs of their members and the support software.
Social PropertiesA social property can be modeled as a network of AT concepts with a
Activity theory (AT) is a paradigm for the analysis of human groups but with a focus on acts.1 It assumes that peoples behavior depends on the physical and sociocultural context in which they live and work.2 At the same time, people interact with and thereby change their environment. These interactive acts are called activities and their contexts activity systems.
The activity is a process driven by peoples needs. These needs represent key objectives, such as keeping in touch with friends. This process is always conceived as a trans-formation of some objects into the outcomes satisfying those needs. For example, the activity of chatting in an OSN transforms its objectsthe people communicatingand its outcome is those people having information of their last talk with friends. The active component carrying out the activity is the subject (here, the people engaged in the chat). Any other element used in the transformation is a tool, such as the software used to chat. Tools mediate the interaction of the subject with the environment.
The social dimension of activities is organized around communitiesgroups of subjects sharing social meanings and artifacts. A community is the members of the OSN. Two social artifacts mediate the relationships within the commu-nity: rules with the subject and the division of labor with the object. Rules include laws, social conventions, and norms ex-ternal to the activity but affecting it. For instance, the cultural norms of polite behavior are rules applying in the OSN. In the transformation process, the division of labor establishes the role of the actors in the community, the power that they hold, and the tasks for which they are responsible. The fact that the administrator can moderate an open forum is an example of division of labor. All the elements of these types related to an activity constitute what we call the activity system.
From the AT point of view, objects, tools, and outcomes can be both mental and physical elements. So, the para-digm considers physical and mental processes. Deciding what to communicate is a mental process, while effectively acting on the interface of the chat software is an example of a physical process.
AT describes social systems as networks of activity sys-tems that share some elements. For instance, an activity
system can produce as its outcome some information that is a tool for another activity system. Subjects simultaneously execute activities in these networks following their own rationality.
Contradictions are conflicts between elements, both within and between activity systems.3 Bad interface design in the chat software (a tool) regarding the user preferences (the subject features) is a contradiction for the chat activity system, and its origin is in a contradiction with the activity system that designs that software. Subjects try to remove contradictions by changing the activity systems, but these changes carry the seed of new conflicts. Thus, contradic-tions are the driver for the evolution of social systems.
AT has been applied to analyze complex human settings in software development.4 Most of these studies retain the original AT practices, which are hard to capture in formal languages and difficult to automate.
We have tried to improve this situation by defining a Unified Modeling Language profile called UML-AT,5 which captures the AT conceptual framework for software de-velopment. Studies on the integration of UML-AT with the agent paradigm support the application of AT to software systems analysis.4 These applications include the defini-tion of social properties to elicit and interpret knowledge about a target software system. Some of these properties can be reused in the context of OSNs, such as those related to user motivation, external social structures, or knowledge sharing.5
References 1. A.N. Leontiev, Activity, Consciousness, and Personality, Prentice-
Hall, 1978. 2. L.S. Vygotsky, Mind and Society, Harvard Univ. Press, 1978. 3. Y. Engestrm, Learning by Expanding, Orienta-Konsultit Oy,
1987. 4. G.Z. Bednyi and D. Meister, The Russian Theory of Activity:
Current Applications to Design and Learning, Lawrence Erlbaum Assoc., 1997.
5. R. Fuentes-Fernndez, J.J. Gmez-Sanz, and J. Pavn, Under-standing the Human Context in Requirements Elicitation, Requirements Eng., vol. 15, no. 3, 2010, pp. 267283.
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given intentional and organizational meaning. These properties describe different aspects of OSNs and their environments, such as the motivation driving users, the kind of social struc-ture a group of connected users has, and their interaction patterns.
The definition of these social prop-erties is the result of a collaborative effort: AT researchers determine the
relevant social features and help in their interpretation, domain experts determine a suitable formulation of properties, and software engi-neers decide what data from the sup-port systems can provide the kind of information required. The struc-ture in Figure 1 is an example of the result of the joint work of these experts.
As Figure 1 shows, each social property has a unique identifier and a generic description. This description contains a general discussion on as-pects such as goals, applicability, and benefits. Besides these common and general aspects, a property is con-sidered in different settingsthat is, particular contexts with specific in-terpretations. For instance, consider a
Figure 1. An example structure defining a setting of social property. The social property represents a general social context, and the setting a particularization. Each setting can be described from different perspectives, changing the explanation and diagrams according to the target audience. Contradictions are conflicts in activity theory, and this figure corresponds to the exchange value contradiction. This contradiction occurs when a subject (the Generator subject) has to produce (using the Generator activity) a product (the Generator product) for a community (represented by the Subject in the community), but the subject considers the obtained benefit (the Generator objective) insufficient compared with a key objective (such as the Individual objective). See the Activity Theory sidebar for the explanation of other terms and concepts.
Slot bindings for perspectives: AT. Individual objective, and so on. Domain. Person goal, and so on. System. Person or agent goal, and so on.
Property Identier: Exchange Value Perspective: AT Domain
This contradiction emerges when a subject has to generate an outcome to be consumed by other members of the community. However, none ofthe generator subjects relevant objectives are satisfied by that outcome. Consequently, the subject who can create the outcome and give it to thecommunity might not have sufficient motivation to do it.
Property specification Related property: Exchange Value Solution
SubjectSubject in thecommunity
ObjectiveObjective in the community
ActivityActivity in thecommunity
Change of role
SubjectSubject in thecommunity
ObjectiveObjective in the community
ActivityActivity in thecommunity
Change of role
The Generator subject is able to produce a Generator product with the Generator activity and, in so doing, satisfy a Generator objective. However, its main objective is the Individual objective, so, depending on circumstances, the subject might not be interested in generating the product. But the Subject in the community needs the product to carry out its Activity in the community.
The Subject in the community can overcome the Exchange value contradiction by using a reward to encourage the Generator subject to execute the task or by making the benefits of its execution explicit. In the first case, the Subject in the community should provide the generator with products that satisfy a relevant need, represented by a new Reward activity for the Subject in the community. That activity generates a reward that directly contributes to generator subjects main objective of the Generator subject. (The dashed circleshows the elements added in the solution.)
Slot bindings for related properties: Exchange value. Any variable. Exchange value solution. The same value as in the original
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property to identify selfish users in a community. Such users provide con-tent of low quality or devote little time to their content editing, and so they add little value. However, there are some differences between the two cases: the action sequences are very similar, but there are different as-pects to measure to detect them. This kind of difference is described by dif-ferent settings of the same property. The description of a setting com-prises UML-AT diagrams, a textual explanation, related social properties, examples of its use, and bindings be-tween variables to indicate what in-formation is related between the dif-ferent diagrams.
The UML-AT diagrams model the situation represented by the setting. They have slots for the name, type, and value of their entities, relation-ships, and properties. The slots can contain fixed values or variables. A setting appears in a given OSN if there is a correspondence between its UML-AT representation and the OSN information. This is essentially a pattern-matching process: both groups of information have the same fixed values, and the setting variables can be instantiated with information from the OSN. When this happens, the property and setting descriptions help to build a social interpretation of that OSN information.
Setting DetailsThe examples of a setting show its ap-plication in previous projects. The related properties are references to set-tings of other properties that can com-plement or be a consequence of the set-ting under consideration. For instance, given a property describing a type of group organization, related properties can show typical workflows within it; given a conflict, a related property can point out a possible solution (the case illustrated by Figure 1).
Although settings allow the descrip-tion of detailed contexts of property application, they are inadequate to cover the different perspectives that experts have on properties. To en-able the active involvement of ana-lysts with different backgrounds, setting descriptions have several per-spectives aimed at different audiences and purposes. The AT perspective is intended for social researchers and describes the intentional and social interpretation of the setting; the do-main perspective is for specific do-mains, translating abstract AT con-cepts to the relevant organizations, laws, workflows, or artifacts; the sys-tem perspective helps map the previ-ous elements to data and function-ality present in the support systems. For instance, the social researcher talks about the activity that satis-fies user needs, while in the domain of a personal OSN the user is updat-ing her or his profile looking for new friends, and the software engineer sees actions that end with new infor-mation in a database.
The glue connecting all these ele-ments is slot bindingspairs of ele-ment names (entities, relationships, and properties) in different diagrams. They specify the correspondences be-tween the slots in different perspec-tives of a setting, or in a setting and its examples and related properties. The bindings for perspectives are necessary for understanding the rela-tionships between the elements in the different views, facilitating their in-terpretation and discussion by the dif-ferent specialists involved in their use. The bindings between a setting and its related properties make it possible for analysts to understand the poten-tial applications of those properties in contexts where the current setting appears. For example, consider-ing the AT perspective, the bindings can indicate that the outcome of a
particular setting can become the sub-ject of a setting of a related property. Finally, the bindings between a set-ting and an example show the instan-tiation of the setting in the particular context that the example describes.
The exchange value contradiction is an example of a social property, and Figure 1 shows a partial descrip-tion of one perspective in one of its settings. This property corresponds to a contradiction (that is, it describes a conflict according to AT): the gen-erator subject is expected to perform an activity that does not further any of his or her main objectivesrepresented by the individual objec-tive. AT studies have described poten-tial solutions for this contradiction, and the right side of Figure 1 models one of them as a related social prop-erty. Related properties are optional, so they do not need to appear for ev-ery setting.
The use of these properties is partly supported by functionality developed for the activity theory assistant (ATA), a software tool able to check the oc-currence of social properties in UML-AT specifications.6 For this purpose, the tool manages a repository of in-formation that includes a catalog of social properties available to perform the analysis and facts gathered about the systemfor instance, its design and logs of user activity.
Recommendation ServiceThe case of a collaborative recom-mendation service can illustrate the use of the social-properties frame-work. In this example, the purpose of the OSN is to build a virtual com-munity where users can share their leisure interests. Users create a pro-file along with their account, which lets the system contact people with similar interests. To make the com-munity attractive, users are expected to actively participate by keeping
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their profiles updated, setting up and taking part in activities with other members, and reporting poten-tial misuses. Users who adopt a pas-sive role impoverish the life of the community.
The OSN has several systems for supporting and analyzing these activities: user applications, monitoring tools, and server systems. Users inter-act with the OSN using desktop soft-ware and webpages. In addition, the service monitors user behavior, creat-ing logs of requests and responses to the central services. The overall goal of these activities is to be able to ad-vise users on how to improve their in-teractions with the systems and the community. Social analysts and soft-ware engineers use social properties and ATA for this analysis.
The first step is seeding the reposi-tory with the OSNs laws and initial facts. Laws are information about the system that is always true. In this case, the laws include the types of profiles and expected user motiva-tions, several interactions by which users can engage in the real world with influence in the OSN, and the design of support systems because they determine the user-system inter-actions and how the systems should behave. The facts are information currently true but that can become false over time. Here, the initial facts include the existence of a default administrator.
The systems workflows include two types of OSN users: the member provides content and mainly inter-acts with peers, and the administra-tor manages the members and social community content. Since both types of users are active elements in the OSN, they are subjects in UML-AT. A member can perform the follow-ing activities: consult information, update information, and notify misuse. The last two activities
improve the service content by gen-erating modified contents as their outcome. An active member should regularly perform these activities to contribute to the OSN. The adminis-trator performs the activity support members, which supervises con-tent and notifications for the commu-nity. This user also performs offline management of the content in the ac-tivity support community. For in-stance, the administrator moves the content to historical archives or per-forms data mining to discover trends.
After monitoring the system for some time, social analysts would want to check the OSN behavior. The first step is to update the repository with new facts according to observa-tions. Here there are several kinds of facts. Observed facts are direct trans-lations of events in the systemfor in-stance, a user pressing a button. The already mentioned laws can describe temporal sequences of activities. Af-ter observation of the initial activities of a sequence, the subsequent activi-ties and their related elements (such as tools, subjects, and outcomes) are as-serted as planned factsfor example, the expected next action of the user after pressing the button. Some ob-served facts do not correspond to any law but do happen now and againfor example, a user updating his or her profile every Sunday. In this case, their future occurrence is asserted as a foreseen fact, which is regarded as less probable than a planned fact. Af-ter a given period without an actual observation that supports a given law or fact, it becomes nonvalid to limit the size of the repository.
Then, engineers and analysts try to detect social properties in the re-pository using the ATA. A corre-spondence with the setting of the exchange value contradiction ap-pears in the OSN; Figure 2a shows its instantiation, and the original
explanation of the setting in Figure 1 interprets it. The OSN member should carry out the activity notify misuse to provide the outcome modified contents to the adminis-trator. The administrator, through the activity support members, pro-cesses this information to improve the services contents. The contradiction occurs because the member is not performing the activity (asserted as a planned fact) despite all its prerequi-sites (subjects and objectives) appear-ing in the repository (as observed in-formation). After a given time-out, the absence of messages in the sup-port system indicates that the activity notify misuse has not been executed, so this planned information becomes nonvalid. As the activity generates the outcome modified contents, it is also marked as nonvalid. Thus, the ATA detects the contradiction, whose interpretation is that the member (the generator subject) is not sufficiently motivated by the community to carry out the action (generator activity). Consequently, the administrator can-not perform the related activity, be-cause its required input (the generator product) is not available.
Figure 2b shows a possible solu-tion to the exchange value contra-diction (see the Related Property in Figure 1). The subjects in the com-munity who benefit from the prod-uct of the generator subject should motivate him or her. They can pro-vide products that satisfy that mem-bers relevant needs or make explicit the contribution of the generator ac-tivity to those needs. For this case, social analysts adopt the second alternative: when the member consults the OSN, the system will show infor-mation about how his or her activi-ties contribute to information in the community. The execution of the ac-tivity support members can trigger a new activity generate statistics,
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which gathers information for re-ports about members activities. When the member demands infor-mation through an activity consult information, the system uses the re-ports to display relevant statistics about the members contributions. In this way, it is expected that members will clearly see the influence of their collaboration in keeping the commu-nity alive. The diagram shows this modification as a contribute pos-itively relation from the outcome report to the members main objec-tive, enjoy the community. Alterna-tively, software engineers can use the reports to design a mechanism of re-wards to members according to their activities, so their involvement no lon-ger depends on subjective perceptions.
The AT catalog of properties and solutions for human conflicts has been a useful guide for building the OSN: social researchers get advice on how to elicit information and deal with members behavior regarding their objectives, and engineers learn
how to develop sound and robust support systems.
The framework for OSN analysis we have proposed has several important features:
It offers a simple and semiautomated way to analyze OSNs because it re-quires simple pattern matching of social properties with information about an OSN. This provides a pre-liminary interpretation of the data, although analysts still need to ana-lyze the results carefully. Other semiautomated methods offer less processed results, leaving data inter-pretation as the exclusive responsi-bility of analysts.
It includes a catalog of ready-to-use social properties based on AT to understand OSNs with multi-ple interacting elements. This cat-alog relieves the analysts work-load, as they dont need to define all the properties of interest, and
they have additional information to guide the analysis.
It aids in understanding what hap-pens inside a support system that generates long information logs during its execution. By using social properties and agent concepts, the framework can summarize com-plete logs as a list of understand-able interpretations.
It can deal with incomplete OSN models. This is essential when dealing with people, as there is no way to get exact models of their behavior. The qualification of the information with certainty degrees (laws, observed, and planned) allows reasoning with un-certainty and the presentation to ana-lysts and engineers of the interpreta-tions that are most likely valid.
The use of nonvalid facts takes into account the evolution of the OSNfor example, adapting to us-ers, changing the networks focus, adding new features to the sup-port system, or automating learn-ing. Moreover, it provides a way to
Figure 2. Exchange value contradiction with users. (a) A diagram showing the correspondence with the contradiction. Members are expected to perform the activity notify misuse so that administrators can execute the activity support members to maintain the social networks quality. However, this member has little interest in that activity, regarding the related objective keep community as insignificant. This conflict constitutes the contradiction. (b) A possible correspondence with the solution. Following activity theory, the solution makes more explicit the benefits of performing the activity notify misuse. It introduces a new product, report, intended to show the member the impact of collaboration, represented by a contribute positively relationship. The patterns include planned facts (within dashed lines), the added elements for the solution (within dotted lines), and observed facts.
ObjectiveEnjoy the community
Change of role
ObjectiveEnjoy the community
Change of role
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discard obsolete information (a task already considered in other studies).
Experiments carried out so far, of which the case study in this article pro-vides an example, show that the process currently faces two main limitations. First, UML-AT needs extensions to include currently unaddressed AT concepts and to support expressing semantic rules about facts, such as possible transitions in the informa-tion states, time-outs, or triggering conditions. Second, the framework needs the information about the OSN to be described with UML-AT. This is feasible for the systems design, as ATA can perform a semiautomated translation of specifications to UML-AT,6 but its a far more difficult task when it involves text analysis. This latter case requires natural-language processing and experts supporting the modeling process. This is an is-sue not only for our work, but for all the research on OSNs. Our ongoing research will partially address the ex-traction of information from micro-blogging sites using templates. A final line of future work is the application of AT contradictions and their solu-tions for detecting conflicts as well as for laws governing OSN behavior.
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t h e a u t h o r SRubn Fuentes-Fernndez is an associate professor of computer science at the Univer-sidad Complutense de Madrid. His primary research interests concern the application of social sciences to the development of software systems regarding group activities. Fuentes-Fernndez has a PhD in computer science from the Universidad Complutense de Madrid. Contact him at email@example.com.
Jorge J. Gmez-Sanz is an associate professor of computer science at the Universidad Complutense de Madrid. His main research interests are agent-oriented software engi-neering and its application. Gmez-Sanz has a PhD in computer science from the Univer-sidad Complutense de Madrid. Contact him at firstname.lastname@example.org.
Juan Pavn is a full professor of computer science at the Universidad Complutense de Madrid and leads the universitys Agent Research Group. His current research interests include model-driven engineering and multiagent systems. Pavn has a PhD in computer science from the Universidad Politcnica de Madrid. Contact him at email@example.com.
Selected CS articles and columns are also available for free at
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