. However, studies that combine different methods are scarce [9]. This holds in particular for studies that focus on relational (using social network analysis) and interpretational information (using for example semantic maps). To our knowledge, studies that employ both methods are lacking. With this paper, we want to contribute to the literature by proposing a way to combine both approaches. We illustrate the approach with data from an online community, and discuss implications for researchers

Groenewegen, P.

Hellsten, I.

Moser, C.

DSpace at VU

The Icing on the Cake – Combining Relational and 
Semantic Methods to Extract Meaning from Online 
Message Board Postings 
Christine Moser 
VU University Amsterdam 
De Boelelaan 1081 
NL-1081 HV Amsterdam 
+31 20 5989149  
c.moser@vu.nl 
Iina Hellsten 
VU University Amsterdam 
De Boelelaan 1081 
NL-1081 HV Amsterdam 
+31 20 5986983 
i.r.hellsten@vu.nl 
Peter Groenewegen 
VU University Amsterdam 
1st line of address 
2nd line of address 
+31 20 5986914 
p.groenewegen@vu.nl 
 
 
ABSTRACT 
In this paper, we propose to combine relational and semantic 
approaches toward studying online communities. In particular, we 
demonstrate how to integrate social network analysis data and 
semantic maps, both extracted from one data set. Integration 
yields insights that lead to more than just a combination of 
insights. Specifically, we find that individuals occupying central 
network positions use distinct word configurations and are 
concerned with very different topics. Thus, we provide a 
relational network with meaning, and discuss how this finding 
might be employed in future research. 
General Terms 
Measurement, Documentation, Human Factors, Languages, 
Theory. 
Keywords 
Online Communities; Social Network Analysis; Semantic Maps; 
Content Analysis; Mixed Methods. 
1. INTRODUCTION 
In recent years, we have seen a rapid growth of online 
communities on the Web. These virtual communities serve as 
socio-technical platforms for various professionals, entrepreneurs 
and serious hobbyists to engage in discussion around a shared 
area of interest. They mostly use these platforms to exchange 
knowledge and expertise. So far, online communities have been 
analysed either via the structure of the communities or the content 
of messages and the motivations of members. We argue that in 
order to gain insight in the dynamics of online communities, we 
have to combine a set of methods that allows for the analysis of 
both the structure as well as the content of communications in 
these communities. Empirical studies in the domain of online 
communities usually employ a single method. Often, motivations 
to participate in these communities were investigated [e.g., 1, 2]. 
Other research focused on behaviour of community members 
[e.g., 3, 4]. Some of these studies employed qualitative methods, 
especially case studies [5-6] and ethnographies [7-8]. However, 
studies that combine different methods are scarce [9]. This holds 
in particular for studies that focus on relational (using social 
network analysis) and interpretational information (using for 
example semantic maps). To our knowledge, studies that employ 
both methods are lacking. With this paper, we want to contribute 
to the literature by proposing a way to combine both approaches. 
We illustrate the approach with data from an online community, 
and discuss implications for researchers. 
2. EARLIER RESEARCH AND 
METHODOLOGICAL POSITIONING 
Recently, several approaches to combining network structure and 
network content have been conducted in information science, 
computer sciences and bibliometrics. Danowski and Cepela [10] 
have conducted automated network analysis of social actors using 
text corpora. Mapping the co-actor networks is similar to the basic 
bibliometrics analysis of co-authorships in scientific publication 
that has flourished in bibliometrics and scientometrics since the 
1980s. Ereteo et al. [11] have specifically focused on combining 
the structure and content of networks by manually adding tags to 
messages. Interestingly, their results suggest that the semantics in 
the networks affect the network structure. There are also efforts to 
develop content based social network analysis [12].  Their main 
interest is in analyzing shared topics between groups. In a similar 
vein, Roth and Cointet [13] have studied the co-evolution of 
social and socio-semantic networks on large-scale networks of 
scientists (co-authorships and co-topics), and between bloggers 
(hyperlinks and topics). Xu and colleagues [14] have combined 
social networks with semantic concepts analysis by comparing the 
networks of researchers to the networks of concept similarity. 
Another step toward the integration of interpretive and statistical 
methods has been undertaken recently, showing that social 
network analysis and discourse analysis can be usefully integrated 
[15]. However, in our analysis we will pay attention to how 
semantic co-word maps are related to the network structure. This 
approach combines two automated analytical processes, focused 
on individual actors in a network. Thus, this approach differs from 
earlier approaches that combine qualitative and statistical methods 
by integrating automated analytical processes. Furthermore, this 
method allows us to zoom in on individual actors, both in terms of 
their structural positioning in the network, as well as the content 
of their individual messages, and thus offers new perspectives that 
build on abovementioned studies. 
 
Permission to make digital or hard copies of all or part of this work for 
personal or classroom use is granted without fee provided that copies are 
not made or distributed for profit or commercial advantage and that 
copies bear this notice and the full citation on the first page. To copy 
otherwise, or republish, to post on servers or to redistribute to lists, 
requires prior specific permission and/or a fee. 
WebSci ‟11, June 14-17, 2011, Koblenz, Germany. 
Copyright held by the authors. 
 
3. SETTINGS AND METHODS 
In order to make our point, we conduct an illustrative study of 
members of one online community of Dutch cake bakers with 
about 13,000 members. First, we conducted a social network 
analysis (SNA) to analyze the networked structure of the 
communications in the community. We selected data from one 
particular day that showed a lot of activity and promised to yield 
good results. In total, 212 actors participated in discussion on that 
day, leaving 1378 messages. We then compiled an affiliation 
matrix: actors were entered in rows, events (defined as topics 
where messages were left) in columns. We defined a network tie 
as simultaneous presence of messages by individual community 
members at the same topic. This affiliation matrix was then 
transformed into a bipartite graph [16] which allowed us to 
calculate member centrality measures using Ucinet 6 [17].  
 
For the next analytical step, we investigated all messages of the 
three most central actors, who posted 67, 85 and 129 messages, 
respectively. We used a co-word based semantic maps approach 
that shows the implicit frames within the discussions [18]. The 
method automatically maps the positions of words in a set of 
documents, on the basis of asymmetrical word-document matrices 
where documents form the rows and the words the columns. 
Salton’s index (cosine) is used for the normalization. The 
positions of words in a set of documents can be considered as the 
unintended results of a set of relations in a network among agents 
or documents [19-20]. The method focuses not only on dyadic co-
occurrences of words but also triadic etc. co-occurrences. For the 
visualization we did not use the co-occurrence matrix but the 
underlying asymmetrical matrix of documents versus words, and 
subsequently computed the distance among the word vectors 
using the vector-space model, that is, using the cosine as a 
similarity measure [21]. 
 
4. FINDINGS 
The first part of our findings consists of centrality measures of all 
actors within the social network. In particular, we focused on 
degree centrality. Table 1 shows that the three most central actors 
indeed occupy the top with quite some distance to the rest of the 
actors1. As these three actors prove to be most active in this 
network, further investigation of their results should yield 
interesting results.  
Table 1. Degree centrality of top 20 actors 
Actor 
Normalized degree 
centrality 
1 7.916 
2 6.406 
3 5.287 
4 4.502 
5 4.154 
6 3.836 
7 3.599 
                                                                
1 We only show the top 20 actors, to give an impression of the 
differences between actors.  
8 3.406 
9 3.318 
10 3.118 
11 2.784 
12 2.777 
13 2.747 
14 2.555 
15 2.540 
16 2.481 
17 2.325 
18 2.155 
19 2.066 
20 1.985 
 
Next, we investigated the semantics of the top three actors’ 
messages using automated mapping of the positions of words in 
the documents of the three most central posters, Actor 1, Actor 2 
and Actor 3, respectively, as mentioned in Table 1. Due to the 
relatively larger number of messages of actor 3, we present a 
semantic map of this actor that contains unique words used three 
times or more, in order to be able to present a clearly arranged 
map. The other two semantic maps present unique words that 
occur twice or more. Colours of nodes in the semantic maps 
indicate different word clusters, whereas their size relates to the 
frequency of words. Finally, tie strength indicates the frequency of 
relations between words.  
 
The semantic map of Actor 1’s messages mainly contains 
admiring comments, such as ‘looks just super’, ‘what a beautiful 
cake’, and supporting comments like ‘well done’, and ‘want to 
bake that this week as well...’. In the semantic map, words such as 
good, great, delicious, wow and ooow occur often as well as the 
smiley big smile, and smiley lol (Figure 1). The semantic map is 
therefore quite coherent. 
 
Figure 1. Semantic map of actor 1’s messages; 61 unique 
words (occurring twice or more often, stopwords removed), 67 
postings, cosine: 0.303 
 
The messages of Actor 2 are more diverse in their semantics. They 
show support, but on a slightly more moderate level, using 
expressions such as ‘what a nice one girl’, ‘can imagine you want 
to try everything now’, ‘unfortunately the photo is not quite as I 
wanted…’. There are more smileys in use than in Actor 1’s 
messages, covering the whole range from smiley big smile, smiley 
yummie to smiley huh (Figure 2). As such, this semantic map 
shows more variation than that of Actor 1, and emphasizes the use 
of smileys. 
 
Figure 2. Semantic map of actor 2’s messages; 62 unique 
words (occurring twice or more often, stopwords removed), 85 
postings, cosine 0.302. 
 
Actor 3 posted more messages than the more central actors 1 and 
2 during our period of analysis. Most messages concern heading 
for a journey (hotel, room, airplane, airport, and suitcase). There 
are also several postings that are probably meant for insiders, such 
as ‘well, I can’t do anything about it that you are jealous’, or 
‘there are things I just cannot post here in the forum smileylol‟ 
(Figure 3). Overall, her semantic map is much more diverse in 
composition than the ones from Actor 1 and 2. 
 
Figure 3. Semantic map of actor 3’s messages; 49 unique 
words (words occurring three times or more, stopwords 
removed), 129 postings, cosine 0.265 
 
When comparing the three semantic maps, several differences 
stand out. Actor 1’s words produce a quite coherent network, 
which mainly consists of compliments and support for others. The 
actual content of her messages remain unclear, she seems to focus 
mostly on other actors’ messages. Actor 2’s messages lead to a 
slightly different picture: although this semantic map is also quite 
coherent, there is more variation (hence a larger semantic map). 
Whereas she also seems to compliment others, she extensively 
uses smileys in her messages. Finally, Actor 3 uses a wider scale 
of words, in particular words related to cakes. This leads to a more 
varied semantic map. Apparently, her use of words is less 
coherent than Actor 1’s and 2’s. 
 
5. DISCUSSION AND CONCLUSION 
Our results show that individuals who occupy central positions in 
a social network use distinct configurations of words .Whereas the 
first investigated member dominantly uses words that serve as 
compliments and support for others, the second member 
extensively employs smiley’s to make her point. Finally, the third 
member uses a wider scale of words, in particular words related to 
cakes. Thus, although the three actors all occupy central positions 
in the social network, their use of words is significantly different. 
This is interesting, because it shows that individuals feature 
different styles when composing messages, and that their 
messages are concerned with very different content. From an 
institutional theory perspective [e.g., 22], the question arises how, 
from a micro-lens, this is possible, considering that this particular 
community is very cohesive and features strong social norms. 
Future research might investigate the social mechanisms that both 
enable and restrain individuals in their use of words when 
composing messages. 
 
Our study shows that a relational and semantic approach to 
analyzing online messages yields rich results. Not only is it 
possible to shed light on the relational structure of a network, 
such as the position of different actors. We also are able to 
investigate the content of messages, using exactly the same data. 
This content analysis provides the network with meaning: it 
informs us about what flows between different actors, in addition 
to knowledge about their connectedness.  
 
This study contributes to the emerging literature arguing for more 
use of cross-disciplinary methodology [e.g., 23]. In particular, it 
provides a point of departure for studies into social networks that 
aim to not only map out their social structure, but also want to 
attach meaning to this structure. Thus, our approach is a first step 
toward a more encompassing method in social network research.  
 
Finally, we open up new perspectives for scholars who are mainly 
concerned with the analysis of text. The rise of Internet 
technology, with its accompanying rush of social media and other 
communication outlets, provides scholars with new possibilities 
concerning data collection and analysis. As we show in this 
article, the same dataset provides us with the possibility to 
perform different kinds of analyses, such as content analysis and 
social network analysis. Scholars who predominantly perform 
content analysis might want to consider extending their approach 
with other possibilities, such as social network analysis.  
 
6. REFERENCES 
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software developers in Open Source projects: an 
Internet-based survey of contributors to the Linux 
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[2] Jeppesen, L.B. and L. Frederiksen, Why do users 
contribute to firm-hosted user communities? The case of 
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[3] Lakhani, K.R. and E. von Hippel, How open source 
software works:“free” user-to-user assistance. 
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[4] Franke, N. and S. Shah, How Communities Support 
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[5] Piller, F., et al., Overcoming Mass Confusion: 
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[6] Ross, D.A.R., Backstage with the Knowledge Boys and 
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[7] Füller, J., G. Jawecki, and H. Muhlbacher, Innovation 
creation by online basketball communities. Journal of 
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[8] O'Mahony, S., Guarding the commons: how community 
managed software projects protect their work. Research 
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[9] Wasko, M. and S. Faraj, Why Should I Share? 
Examining Social Capital and Knowledge Contribution 
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The icing on the cake - combining relational and semantic methods to extract meaning from online message board postings

In recent years, we have seen a rapid growth of online communities on the Web. These virtual communities serve as socio-technical platforms for various professionals, entrepreneurs and serious hobbyists to engage in discussion around a shared area of interest. They mostly use these platforms to exchange knowledge and expertise. So far, online communities have been analysed either via the structure of the communities or the content of messages and the motivations of members. We argue that in order to gain insight in the dynamics of online communities, we have to combine a set of methods that allows for the analysis of both the structure as well as the content of communications in these communities. Empirical studies in the domain of online communities usually employ a single method. Often, motivations to participate in these communities were investigated [e.g., 1, 2]. Other research focused on behaviour of community members [e.g., 3, 4]. Some of these studies employed qualitative methods, especially case studies [5-6] and ethnographies [7-8]. However, studies that combine different methods are scarce [9]. This holds in particular for studies that focus on relational (using social network analysis) and interpretational information (using for example semantic maps). To our knowledge, studies that employ both methods are lacking. With this paper, we want to contribute to the literature by proposing a way to combine both approaches. We illustrate the approach with data from an online community, and discuss implications for researchers

VU Research Portal

VU Research PortalThe icing on the cake - combining relational and semantic methods to extract meaningfrom online message board postingsMoser, C.; Hellsten, I.; Groenewegen, P.published inProceedings of the ACM WebSci'112011document versionPublisher's PDF, also known as Version of recordLink to publication in VU Research Portalcitation for published version (APA)Moser, C., Hellsten, I., & Groenewegen, P. (2011). The icing on the cake - combining relational and semanticmethods to extract meaning from online message board postings. In Proceedings of the ACM WebSci'11 (pp. 1-4). http://journal.webscience.org/468/1/165_paper.pdfGeneral rightsCopyright and moral rights for the publications made accessible in the public portal are retained by the authors and/or other copyright ownersand it is a condition of accessing publications that users recognise and abide by the legal requirements associated with these rights.            • Users may download and print one copy of any publication from the public portal for the purpose of private study or research.            • You may not further distribute the material or use it for any profit-making activity or commercial gain            • You may freely distribute the URL identifying the publication in the public portal ?Take down policyIf you believe that this document breaches copyright please contact us providing details, and we will remove access to the work immediatelyand investigate your claim.E-mail address:vuresearchportal.ub@vu.nlDownload date: 23. May. 2021The Icing on the Cake – Combining Relational and Semantic Methods to Extract Meaning from Online Message Board Postings Christine Moser VU University Amsterdam De Boelelaan 1081 NL-1081 HV Amsterdam +31 20 5989149  c.moser@vu.nl Iina Hellsten VU University Amsterdam De Boelelaan 1081 NL-1081 HV Amsterdam +31 20 5986983 i.r.hellsten@vu.nl Peter Groenewegen VU University Amsterdam 1st line of address 2nd line of address +31 20 5986914 p.groenewegen@vu.nl   ABSTRACT In this paper, we propose to combine relational and semantic approaches toward studying online communities. In particular, we demonstrate how to integrate social network analysis data and semantic maps, both extracted from one data set. Integration yields insights that lead to more than just a combination of insights. Specifically, we find that individuals occupying central network positions use distinct word configurations and are concerned with very different topics. Thus, we provide a relational network with meaning, and discuss how this finding might be employed in future research. General Terms Measurement, Documentation, Human Factors, Languages, Theory. Keywords Online Communities; Social Network Analysis; Semantic Maps; Content Analysis; Mixed Methods. 1. INTRODUCTION In recent years, we have seen a rapid growth of online communities on the Web. These virtual communities serve as socio-technical platforms for various professionals, entrepreneurs and serious hobbyists to engage in discussion around a shared area of interest. They mostly use these platforms to exchange knowledge and expertise. So far, online communities have been analysed either via the structure of the communities or the content of messages and the motivations of members. We argue that in order to gain insight in the dynamics of online communities, we have to combine a set of methods that allows for the analysis of both the structure as well as the content of communications in these communities. Empirical studies in the domain of online communities usually employ a single method. Often, motivations to participate in these communities were investigated [e.g., 1, 2]. Other research focused on behaviour of community members [e.g., 3, 4]. Some of these studies employed qualitative methods, especially case studies [5-6] and ethnographies [7-8]. However, studies that combine different methods are scarce [9]. This holds in particular for studies that focus on relational (using social network analysis) and interpretational information (using for example semantic maps). To our knowledge, studies that employ both methods are lacking. With this paper, we want to contribute to the literature by proposing a way to combine both approaches. We illustrate the approach with data from an online community, and discuss implications for researchers. 2. EARLIER RESEARCH AND METHODOLOGICAL POSITIONING Recently, several approaches to combining network structure and network content have been conducted in information science, computer sciences and bibliometrics. Danowski and Cepela [10] have conducted automated network analysis of social actors using text corpora. Mapping the co-actor networks is similar to the basic bibliometrics analysis of co-authorships in scientific publication that has flourished in bibliometrics and scientometrics since the 1980s. Ereteo et al. [11] have specifically focused on combining the structure and content of networks by manually adding tags to messages. Interestingly, their results suggest that the semantics in the networks affect the network structure. There are also efforts to develop content based social network analysis [12].  Their main interest is in analyzing shared topics between groups. In a similar vein, Roth and Cointet [13] have studied the co-evolution of social and socio-semantic networks on large-scale networks of scientists (co-authorships and co-topics), and between bloggers (hyperlinks and topics). Xu and colleagues [14] have combined social networks with semantic concepts analysis by comparing the networks of researchers to the networks of concept similarity. Another step toward the integration of interpretive and statistical methods has been undertaken recently, showing that social network analysis and discourse analysis can be usefully integrated [15]. However, in our analysis we will pay attention to how semantic co-word maps are related to the network structure. This approach combines two automated analytical processes, focused on individual actors in a network. Thus, this approach differs from earlier approaches that combine qualitative and statistical methods by integrating automated analytical processes. Furthermore, this method allows us to zoom in on individual actors, both in terms of their structural positioning in the network, as well as the content of their individual messages, and thus offers new perspectives that build on abovementioned studies.  Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. WebSci ‟11, June 14-17, 2011, Koblenz, Germany. Copyright held by the authors.  3. SETTINGS AND METHODS In order to make our point, we conduct an illustrative study of members of one online community of Dutch cake bakers with about 13,000 members. First, we conducted a social network analysis (SNA) to analyze the networked structure of the communications in the community. We selected data from one particular day that showed a lot of activity and promised to yield good results. In total, 212 actors participated in discussion on that day, leaving 1378 messages. We then compiled an affiliation matrix: actors were entered in rows, events (defined as topics where messages were left) in columns. We defined a network tie as simultaneous presence of messages by individual community members at the same topic. This affiliation matrix was then transformed into a bipartite graph [16] which allowed us to calculate member centrality measures using Ucinet 6 [17].   For the next analytical step, we investigated all messages of the three most central actors, who posted 67, 85 and 129 messages, respectively. We used a co-word based semantic maps approach that shows the implicit frames within the discussions [18]. The method automatically maps the positions of words in a set of documents, on the basis of asymmetrical word-document matrices where documents form the rows and the words the columns. Salton’s index (cosine) is used for the normalization. The positions of words in a set of documents can be considered as the unintended results of a set of relations in a network among agents or documents [19-20]. The method focuses not only on dyadic co-occurrences of words but also triadic etc. co-occurrences. For the visualization we did not use the co-occurrence matrix but the underlying asymmetrical matrix of documents versus words, and subsequently computed the distance among the word vectors using the vector-space model, that is, using the cosine as a similarity measure [21].  4. FINDINGS The first part of our findings consists of centrality measures of all actors within the social network. In particular, we focused on degree centrality. Table 1 shows that the three most central actors indeed occupy the top with quite some distance to the rest of the actors1. As these three actors prove to be most active in this network, further investigation of their results should yield interesting results.  Table 1. Degree centrality of top 20 actors Actor Normalized degree centrality 1 7.916 2 6.406 3 5.287 4 4.502 5 4.154 6 3.836 7 3.599                                                                 1 We only show the top 20 actors, to give an impression of the differences between actors.  8 3.406 9 3.318 10 3.118 11 2.784 12 2.777 13 2.747 14 2.555 15 2.540 16 2.481 17 2.325 18 2.155 19 2.066 20 1.985  Next, we investigated the semantics of the top three actors’ messages using automated mapping of the positions of words in the documents of the three most central posters, Actor 1, Actor 2 and Actor 3, respectively, as mentioned in Table 1. Due to the relatively larger number of messages of actor 3, we present a semantic map of this actor that contains unique words used three times or more, in order to be able to present a clearly arranged map. The other two semantic maps present unique words that occur twice or more. Colours of nodes in the semantic maps indicate different word clusters, whereas their size relates to the frequency of words. Finally, tie strength indicates the frequency of relations between words.   The semantic map of Actor 1’s messages mainly contains admiring comments, such as ‘looks just super’, ‘what a beautiful cake’, and supporting comments like ‘well done’, and ‘want to bake that this week as well...’. In the semantic map, words such as good, great, delicious, wow and ooow occur often as well as the smiley big smile, and smiley lol (Figure 1). The semantic map is therefore quite coherent.  Figure 1. Semantic map of actor 1’s messages; 61 unique words (occurring twice or more often, stopwords removed), 67 postings, cosine: 0.303  The messages of Actor 2 are more diverse in their semantics. They show support, but on a slightly more moderate level, using expressions such as ‘what a nice one girl’, ‘can imagine you want to try everything now’, ‘unfortunately the photo is not quite as I wanted…’. There are more smileys in use than in Actor 1’s messages, covering the whole range from smiley big smile, smiley yummie to smiley huh (Figure 2). As such, this semantic map shows more variation than that of Actor 1, and emphasizes the use of smileys.  Figure 2. Semantic map of actor 2’s messages; 62 unique words (occurring twice or more often, stopwords removed), 85 postings, cosine 0.302.  Actor 3 posted more messages than the more central actors 1 and 2 during our period of analysis. Most messages concern heading for a journey (hotel, room, airplane, airport, and suitcase). There are also several postings that are probably meant for insiders, such as ‘well, I can’t do anything about it that you are jealous’, or ‘there are things I just cannot post here in the forum smileylol‟ (Figure 3). Overall, her semantic map is much more diverse in composition than the ones from Actor 1 and 2.  Figure 3. Semantic map of actor 3’s messages; 49 unique words (words occurring three times or more, stopwords removed), 129 postings, cosine 0.265  When comparing the three semantic maps, several differences stand out. Actor 1’s words produce a quite coherent network, which mainly consists of compliments and support for others. The actual content of her messages remain unclear, she seems to focus mostly on other actors’ messages. Actor 2’s messages lead to a slightly different picture: although this semantic map is also quite coherent, there is more variation (hence a larger semantic map). Whereas she also seems to compliment others, she extensively uses smileys in her messages. Finally, Actor 3 uses a wider scale of words, in particular words related to cakes. This leads to a more varied semantic map. Apparently, her use of words is less coherent than Actor 1’s and 2’s.  5. DISCUSSION AND CONCLUSION Our results show that individuals who occupy central positions in a social network use distinct configurations of words .Whereas the first investigated member dominantly uses words that serve as compliments and support for others, the second member extensively employs smiley’s to make her point. Finally, the third member uses a wider scale of words, in particular words related to cakes. Thus, although the three actors all occupy central positions in the social network, their use of words is significantly different. This is interesting, because it shows that individuals feature different styles when composing messages, and that their messages are concerned with very different content. From an institutional theory perspective [e.g., 22], the question arises how, from a micro-lens, this is possible, considering that this particular community is very cohesive and features strong social norms. Future research might investigate the social mechanisms that both enable and restrain individuals in their use of words when composing messages.  Our study shows that a relational and semantic approach to analyzing online messages yields rich results. Not only is it possible to shed light on the relational structure of a network, such as the position of different actors. We also are able to investigate the content of messages, using exactly the same data. This content analysis provides the network with meaning: it informs us about what flows between different actors, in addition to knowledge about their connectedness.   This study contributes to the emerging literature arguing for more use of cross-disciplinary methodology [e.g., 23]. In particular, it provides a point of departure for studies into social networks that aim to not only map out their social structure, but also want to attach meaning to this structure. Thus, our approach is a first step toward a more encompassing method in social network research.   Finally, we open up new perspectives for scholars who are mainly concerned with the analysis of text. The rise of Internet technology, with its accompanying rush of social media and other communication outlets, provides scholars with new possibilities concerning data collection and analysis. 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The icing on the cake - combining relational and semantic methods to extract meaning from online message board postings

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