[IEEE 2012 International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2012) - Istanbul (2012.08.26-2012.08.29)] 2012 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining - Dissemination Patterns and Associated Network Effects of Sentiments in Social Networks

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<ul><li><p>Dissemination Patterns and Associated Network Effects of Sentiments in Social Networks </p><p>Robert Hillmann Technische Universitt Berlin </p><p>Chair of Systems Analysis and IT Berlin, Germany </p><p>r.hillmann@tu-berlin.de </p><p>Matthias Trier Copenhagen Business School </p><p>Department of IT Management Frederiksberg / Copenhagen, Denmark </p><p>mt.itm@cbs.dk </p><p>AbstractCommunication in online social networks has been analyzed for some time regarding the expression of sentiments. So far, very little is known about the relationship between sentiments and network emergence, dissemination patterns and possible differences between positive and negative sentiments. The dissemination patterns analyzed in this study consist of network motifs based on triples of actors and the ties among them. These motifs are associated with common social network effects to derive meaningful insights about dissemination activities. The data basis includes several thousand social networks with textual messages classified according to embedded positive and negative sentiments. Based on this data, sub-networks are extracted and analyzed with a dynamic network motif analysis to determine dissemination patterns and associated network effects. Results indicate that the emergence of digital social networks exhibits a strong tendency towards reciprocity, followed by the dominance of hierarchy as an intermediate step leading to social clustering with hubs and transitivity effects for both positive and negative sentiments to the same extend. Sentiments embedded in exchanged textual messages do only play a secondary role in network emergence and do not express differences regarding the emergence of network patterns. </p><p>Social Network Analysis, Dynamic Network Motif Analysis, Sentiment Dissemination, Networking Effects, Triads </p><p>I. INTRODUCTION The presence of sentiments and emotions in online social </p><p>networks has been studied for some time in the research of computer-mediated communication (CMC). The study from Rice and Love [1] concluded, that CMC does in fact allow for the exchange of emotions in online environments despite the absence of non-verbal communication parts. In 2007, Derks et al. [2] have examined differences that can be identified regarding the expression of emotions in face-to-face communication and CMC. Results confirmed the findings of Rice and Love. Emotions are abundant in CMC and there is no indication that CMC is to be understood as an impersonal medium or that it is difficult for the user to communicate emotions online. </p><p>Subsequent research further confirmed the existence of emotions and sentiments in various online-based </p><p>communications such as discussion boards, micro-blogging services and other social network applications. Results imply that collective emotional states can be found in social networks and certain real world events and their influences within social networks can be measured. Furthermore, positive and negative events have a significant immediate and specific effect on public mood in social networks [3-5]. </p><p>Despite the versatile research covering presence of sentiments and emotions in online context, there has only been limited research regarding the dissemination patterns of sentiments in online environments utilizing a network perspective. This work uses a novel dynamic network motif approach to analyze sentiment dissemination patterns in online social networks. </p><p>II. THEORETICAL FRAMEWORK Social networks in general are based on human </p><p>interaction and evolve over time with the activities and affiliation of their members. In typical online environments, looking at exchanged textual messages among users over time can be used to derive network structures. This social acting however is influenced by a combination of actor- and network topology-based factors that are called social network effects, which represent an important differentiating factor in comparison with other, non-human network types [6, 7]. Such social network effects describe small interaction paradigms that influence emerging social network topologies. </p><p>The dissemination patterns analyzed in this study consist of network motifs, based on triples of actors and the ties among them. To derive meaningful insights and expectations about the relationship between sentiments and underlying communication mechanisms, the dissemination patterns are associated with social network effects. To identify differences regarding the influence of positive and negative sentiments, sentiment-based sub-networks are used. These sub-networks are extracted by only utilizing messages with a certain sentiment type as basis for the network topology. </p><p>Social network effects which can be identified within the network topology are reciprocity, interpreted as the trend to establish bi-directional friendship relations, hierarchy or popularity, expressing the role of central actors in the </p><p>2012 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining</p><p>978-0-7695-4799-2/12 $26.00 2012 IEEEDOI 10.1109/ASONAM.2012.88</p><p>510</p><p>2012 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining</p><p>978-0-7695-4799-2/12 $26.00 2012 IEEEDOI 10.1109/ASONAM.2012.88</p><p>511</p></li><li><p>network and transitivity, which can be seen as the process of friends of me becoming friends and is also known as triadic closure [7-9]. </p><p>In general, human communication tends to be mutual [10]. Studies have shown that reciprocal network effects in human communication do play a major role in online learning networks [11]. Ammann [12] has identified reciprocity as a indispensable precondition of community formation in accordance with other studies who have shown that during online discussions, the underlying communication network structure includes a significant tendency towards bi-directional links. The tendency is not to establish new communication ties but the participants continue discussions with previous partners which increases reciprocity [13]. </p><p>Despite this general trend towards reciprocity, there are few people within social networks that contribute more comments, posts or messages than others. This unequal distribution has an influence on the underlying network structure. Users with a high share of participation tend to have an increased number of communication partners, leading to unequal node degree distributions. Hence, social networks unveil a trend towards hierarchy [14]. The effect of social prestige [15] which is expressed by direct connections to certain nodes with higher level of hierarchy refers in graphical terms to the number of incoming arcs on vertices also supports theories of hierarchical structures [9]. </p><p>The presence of transitivity or triadic closure was also found in various studies. Schaefer et al. [7] have analyzed the friendship network formation among preschool children. According to their findings, reciprocity, hierarchy and triadic closure all shaped the formation of preschool childrens networks. </p><p>Panzarasa, Opsahl et al. [16] have analyzed the patterns and dynamics of users behavior of an online community. According to their findings, the emerging network within a student social network platform is not constructed uniformly over time. Within the beginning phase of network growth, the vast majority of bi-directional relationships are reciprocated as soon as a single message has been sent. This network growth is followed by structural stability exhibiting network use and reinforcement of existing relationships. Within the development process of the network, the study also found characteristics of scale-free [17, 18] networks with few users having a higher popularity and are being contacted more often than others. This leads to higher-than-average number of acquaintances and an unequal node degree distribution. Despite this reciprocity and hierarchy, the early evolution of the network is characterized by increased density and the development of small clusters merging into a large connected component that indicates the presence of transitivity in generating social clusters. </p><p>These findings are in accordance with results from the aforementioned study from Schaefer, Light et al. [7]. The influence of reciprocity in childrens networks was constantly present, whereas popularity and triadic closure became important within the later network development. Based on these sources, the following hypothesis is stated: </p><p>Hypothesis 1) The emergence of online social networks exhibits a strong tendency towards reciprocity, followed by the dominance of hierarchy as an intermediate step leading to social clustering with hubs and transitivity effects. </p><p>With respect to the focus of this work regarding potential differences of positive and negative sentiments regarding the expression of network effects in the underlying network topology, only few literature insights are available. Heiders [19] classic theory of social balance is focused on the distribution and arrangement of positive and negative relationships in social networks. The balance theory describes certain small structural patterns and common principals that lead to stabile network structures on the basis of the friend of my friend is my friend and the enemy of my friend is my enemy. Heiders social balance theory describes the interplay of positive and negative relationships in network patterns among triples of nodes. A balanced situation is described as either only positive relations or two negative and one positive relationship. The flip cases with three negative or two positive and one negative relation are described as unbalanced, indicating instability and a tendency towards variation [19]. </p><p>The applicability of Heiders social balance theory was analyzed by Leskovec, Huttenlocher et al. [20]. This study is focused on the prediction of positive and negative links in online social networks. Results support the applicability of the social balance theory and the presence of the proposed structural patterns within online networks. </p><p>In this work, sentiments exchanged in messages between users of social networks are projected onto network edges. Based on the asymmetrical patterns from social balance theory and the expected instability described above, possible differences between positive and negative network patterns regarding hierarchy and transitivity are expected. </p><p>Hypothesis 2) The analysis of exchanged sentiments within online social networks exhibit differences regarding the occurrence of small patterns expressing hierarchy and transitivity in positive and negative sub-networks. </p><p>III. DATA FOR ANALYSIS Due to the abstract level of our propositions, the data </p><p>basis covers more than twelve thousand networks from five different sources including data from discussion forums, internet relay chats, micro-blogging services and newsgroups. Each dataset (DS) has a specific underlying data structure, determined by certain technical properties of the internet service in use. To overcome the disadvantages of heterogeneous source data structures, all data was transferred into a coherent event-based data model based on the approach of Trier [21]. The data model used for event-driven network analysis differs from other network data models and has a strong focus on network dynamics [21]. The data model consists of three main elements: networks, nodes and communication events, so called linkevents. The linkevents can be generally described as human interaction in social networks and are based on exchanged textual messages among users. For each dataset these events are of different nature, e.g. forum posts, chat-messages, newsgroup comments or micro-blogging posts. This perspective change </p><p>511512</p></li><li><p>from the original online discourse to event-driven social networks allows the analysis of social networks independent from their respective technical idiosyncrasies of specific web pages or communication applications. </p><p>The forum discussions are retrieved from the BBC website (DS I) as well as the online portal Digg.com (DS II). The dataset covers all discussions from seven BBC message boards covering a time span from 2005 till 2009. The Digg.com forum data is a complete crawl of all story related discussions and covers the months February, March and April of the year 2009. The IRC dataset (DS III) includes chat interaction from the Ubuntu e-community and contains chat dialog recordings from 57 different communication channels and covers a period between summer 2005 and 2010. The micro-blogging dataset (DS IV) is based on Twitter posts from February 2010. The newsgroups dataset (DS V) includes a corpus of Usenet newsgroups with the complete posting history for several Austrian newsgroups from 1995 to summer 2010. All together, the data basis includes more than 12.000 social networks. </p><p>IV. ANALYSIS METHODOLOGY As a precedent step, the exchanged linkevents in all </p><p>networks have been analyzed regarding their embedded sentiments. That was done with special developed classifiers that represent the state-of-the-art in sentiment classification and were developed as part of the European FP7 project Collective Emotions in Cyberspace [22, 23]. The basic schema differs between four disjoint categories of sentiments. Every analyzed and tagged textual message can either be of positive, negative, both positive and negative or neutral nature. Within the limited scope of this work, only positive and negative messages are used for the analysis. </p><p>To study possible differences regarding the relationship between sentiments and social network effects, the upcoming network analysis is done for the positive and negative sentiment-based sub-networks. These sub-networks are filtered out of the complete network by only interpreting linkevents expressing a certain sentiment property as part of the network topology (see Fig. 1). </p><p>The analysis rests on methods of the triad analysis which were introduced to the methodological repertoire of structural analysis in the social sciences by Wasserman and Faust [24]. The method is based on the assumption that different triad formations indicate certain sociological properties of the actor configuration. Triads are triples of actors and the ties among them. Due to the directional character of the ties, there are 16 isomorphism classes for the altogether 64 different triad states (see Fig. 2). </p><p> Figure 1. Sentiment-based sub-network extraction </p><p> Figure 2. </p><p>Isomorphic triad classes [24] </p><p>Based on these structural parts, a network motif analysis is performed. Initially described as triadic census analysis in the same sociological textbook of 1994, the discipline of physics adopted the same method under the name of motif analysis. This network motif analysis is a wide-spread method of sociological research and can be used to discover general profile properties of complex networks [25, 26]. The basic idea interprets the complete network as a collection of overlapping local structures [9]. It is known that many networks share global features like a po...</p></li></ul>


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