Dissemination Patterns and Associated Network Effects of Sentiments in Social Networks

Robert Hillmann Technische Universität Berlin

Chair of Systems Analysis and IT Berlin, Germany

r.hillmann@tu-berlin.de

Matthias Trier Copenhagen Business School

Department of IT Management Frederiksberg / Copenhagen, Denmark

mt.itm@cbs.dk

Abstract—Communication 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.

Social Network Analysis, Dynamic Network Motif Analysis, Sentiment Dissemination, Networking Effects, Triads

I. INTRODUCTION The presence of sentiments and emotions in online social

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.

Subsequent research further confirmed the existence of emotions and sentiments in various online-based

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].

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.

II. THEORETICAL FRAMEWORK Social networks in general are based on human

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.

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.

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

2012 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining

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2012 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining

978-0-7695-4799-2/12 $26.00 © 2012 IEEE

DOI 10.1109/ASONAM.2012.88

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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].

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].

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].

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 children’s networks.

Panzarasa, Opsahl et al. [16] have analyzed the patterns and dynamics of user’s 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.

These findings are in accordance with results from the aforementioned study from Schaefer, Light et al. [7]. The influence of reciprocity in children’s 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:

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.

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. Heider’s [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”. Heider’s 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].

The applicability of Heider’s 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.

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.

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.

III. DATA FOR ANALYSIS Due to the abstract level of our propositions, the data

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

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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.

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.

IV. ANALYSIS METHODOLOGY As a precedent step, the exchanged linkevents in all

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.

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).

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).

Figure 1. Sentiment-based sub-network extraction

Figure 2. Isomorphic triad classes [24]

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 power-law degree distribution [27] or a small world phenomena [28] that can be identified with a network motif analysis. If certain local configurations appear more often than expected by chance, an underlying hypothesis can guide to some characteristics and it is possible to link the network structure to the behavior of actors in the network [9, 29].

The analysis performed in this paper is based on the network motif analysis but features a new methodology introduced to the social network analysis toolset. The composition of network links based on directed linkevents allows for the study of the temporal occurrence of network motifs. Previous studies using a network motif analysis are focused on the static, final network topology. In comparison, our approach utilizes the timestamp of exchanged linkevents to reconstruct the network topology emergence and study the dynamic development.

Each of the 16 triad classes introduced above represents a certain foregone growth. Every triad state (according to Fig. 2) in the network structure includes the previous communication among these three nodes. At the beginning of the network emergence, all triads start as three unconnected nodes with the initial triad state 1. Whenever a communication within these triples of nodes occurs and a new network link is established, the triad state increases. There are 6 possible directed communication links within every triad. In a full-connected network, all triads reach state 16. Depending on the structures of the triad classes, several growth paths from state 1 to state 16 are possible. The structure of possible growth paths consists of seven layers. Each layer corresponds to the number of existing connections within the triad. In the top layer, zero connections are established; in the second layer one link is established and so one. Every time a triad state change occurs, the particular triad moves one level further down. New network links can often arise at multiple positions within the triads. Hence, the majority of triad states exhibit several incoming and outgoing paths. Fig. 3 shows all possible triad state transitions.

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Except the classes 1 and 16, the isomorphic triad classes from Fig. 2 subsume several possible topological link configurations as they can be mirrored and rotated but still belong to the same class [24]. Based on these triad class configurations, multiple outgoing links do not necessarily have equal transition probabilities. Assuming a random addition of network links, each triad state transition can be associated with a certain mathematical probability. These probabilities are associated to links in Fig. 3.

The aforementioned network effects considered in this analysis are reciprocity, hierarchy and transitivity. In addition to these social network effects, the chain/cycle effect can be associated to certain triad states but does not represent a typical social formation pattern and is rather an expression of randomness. Fig. 4 includes all possible triad state transitions sorted according to the seven aforementioned layers. For each of the non-trivial transitions between triad state 2 and 14, the aforementioned social network effects are associated with the triad state transitions. Based on the temporal occurrence, the network effects are distinguished according to primary, secondary and tertiary effects.

Within the analysis, all networks from all five datasets are analyzed regarding the occurrence of triad state transitions for both the positive and negative sub-networks. Possible derivations allow drawing conclusions on the relationship between sentiments and social network effects. For every network, the actual distribution (% share) of triad state transitions is compared with the expectations derived from mathematical probabilities shown in Fig. 3. Performing a t-test with a significance level of 99% ensures the statistical significance of possible derivations.

Figure 4. Triad transitions & associated network effects

V. RESULTS Tables 1 and 2 contain the result of the dynamic network

motif analysis. The statistical significance of derivations regarding the distribution of triad state transitions has been

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determined with a t-test. For each network and for all relevant transitions, the expected value, average occurrence, standard derivation, number of samples, the probability and a trend indicator has been computed. Table I contains a small excerpt including results from four transitions within the negative sub-network from DS III. In this example, 84.45% of all triads within the negative sub-networks from dataset III have changed from triad state 2 to 3. This is a significant upward derivation from the 20% expectation.

The complete results of the dynamic network motif analysis for both the positive and negative sub-networks from all five datasets (DS I – V) are presented in Table II. Due to limited space, the precise data shown above is suppressed and only the resulting trends, based on a t-test (a = 0.99) are shown.

Results of the first triad state transitions unveil a strong tendency towards reciprocity. The majority of all transitions developed towards triad state 3, except within the positive part of the micro-blogging dataset. DS IV and V exhibit a trend towards incoming hierarchy. No dataset includes a significant share of transitions expressing outgoing hierarchy or the chain / cycle effect. Within the second transition step, results express a slightly mixed picture but also allows for relatively unambiguous conclusions.

TABLE I. EXAMPLE RESULTS (NEGATIVE SUB-NETWORK / DS III)

Trans. Eff. Exp. % AVG % SD N P T 2 > 3 R 0,2 0,8445 0,0070 1346 >0.01 � 2 > 4 HO 0,2 0,0899 0,0059 1346 >0.01 � 2 > 5 HI 0,2 0,0188 0,0010 1346 >0.01 � 2 > 6 CC 0,4 0,0466 0,0033 1346 >0.01 �

* Results based on t-test with a = 0.99

TABLE II. RESULTS OF DYNAMIC NETWORK MOTIF ANALYSIS

Trs. Eff. Negative Sub-Network

Positive Sub-Network

I II III IV V I II III IV V 2>3 R � � � � � � � � � � 2>4 HO � � � � � � � � � � 2>5 HI � � � � � � � � � � 2>6 CC � � � � � � � � � � 3>7 HI � � � � � � � � � � 3>8 HO � � � � � � � � � � 4>8 R � � � � � � � � � � 4>9 T � � � � � � � � � � 5>7 R � � � - � � � � � � 5>9 T � � � - � � � � � � 6>7 R � � � � � � � � � � 6>8 R � � � � � � � � � = 6>9 T � � � � � � � � = �

6>10 CC � � � � � � � � � � 7>11 R � � � - � � � � � � 7>12 HO,T � � � - � � � � � � 7>14 T � � � - � � � � � � 8>11 R � � � � � � � � � � 8>13 HI � � � � � � � � � � 8>14 T � � � � � � � � � � 9>12 R � � � � � � � � = � 9>13 R � � � � � � � � � � 9>14 R � � � � � � � � � �

*Results based on t-test with a = 0.99

Triads of type 3 expressing reciprocity indicate a clear tendency developing towards incoming hierarchy as the second network effect. Triads including incoming or outgoing hierarchy from previous transitions (triad state 4 and 5) exhibit a trend towards the expression of reciprocity within the next transition. Triads of type 6 exhibit a strong tendency towards expression of reciprocity.

Within the third transition step, the majority of triads of type 7 and 8 transit towards state 11 expressing reciprocity. All networks exhibit a significant decreased occurrence of transitions expressing transitivity.

Regarding the temporal occurrence and hierarchical order of social network effects, reciprocity is the network effect having the most influence on social network emergence. Incoming hierarchy can be seen as the secondary influence effect. The presence of transitivity is suppressed in online social networks and the occurrence differs among the datasets. The dataset II and IV show transitivity effects, whereas all other datasets express a significant absence of this network effect. Hypothesis 1 predicting the temporal order of social network effects can be supported.

The second hypothesis is focused on possible derivations between positive and negative sentiment-based sub-networks. Results unveil a clear picture. The influence and expression of social network effects differs slightly among the five datasets, however, within a specific dataset, the majority of triad state transitions do not exhibit derivations between different sentiment-based sub-networks. Hence, hypothesis 2 must be rejected.

VI. INTERPRETATION The trend to establish bi-directional relationships is a

dominating factor in the emergence of online social networks. This tendency towards reciprocity is present in a broad variety of online social interaction. The early development of online social networks is characterized with likewise established bi-directional relationships.

Hierarchy with incoming network links is the second influence factor expressing human behavioral patterns in online environments. People with high node degree can be seen as influential within the network. The interesting finding is that nodes with a high node degree are being addressed by other nodes rather than addressing other people pro-active. One can assume that within the development of online social networks, users having a central position in the final network topology gain their position with an early contribution to the network and afterwards establish reciprocal connections with other people that address them.

Triadic closure or transitivity, the development of friends of me becoming friends, interpreted as the emergence of social clusters is not uniformly present in online social networks. This leads to the finding that different online applications with divergent purpose do not in general support or enable clustering but instead transitivity is more context dependent and cannot be found in general.

A major result is that the dynamic influence of network effects within the network emergence must be seen as a property of the networks and the context rather than being influenced by sentiments exchanged among users. The

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dynamic network motif analysis performed in this paper unveils that 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. This is in partially inconsistency with Heider’s social balance theory that proposes differences regarding the occurrence of certain positive and negative actor configurations. The large-scale analysis of exchanged sentiments in online networks can therefore not support this theory. Possible explanations are either that the derivations between different sentiments are not clear enough or that the conclusion from exchanged sentiments onto affective actor configurations is too vague.

VII. CONCLUSIONS This study is focused on the relationship between

sentiments embedded in exchanged messages among users of online social networks and patterns of network emergence. The analysis performed in this paper allows for the identification and temporal ordering of social network effects mapped onto different dissemination patterns within the network development. Previous research from social science predicting a tendency towards reciprocity and hierarchy in social networks can be supported. Results show 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. Results do not exhibit differences regarding the network structure of positive and negative sentiment-based sub-networks leading to the conclusion, that sentiments can only be seen as an indirect influence factor for network emergence.

The methodology introduced in this paper is a suitable basis for studying the dynamic influence of network effects in emerging social networks. In comparison to previous static analyses, the dynamic network motif analysis allows for the precise study of dissemination patterns and associated network effects within network emergence that lead to final structures.

ACKNOWLEDGMENT This work was supported by EU FP7; Theme 3: Science

of complex systems for socially intelligent ICT: Project Collective Emotions in Cyberspace – CYBEREMOTIONS

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