Dynamics of the structure of social interaction in the professional online community of sociologists (the example of online community of sociologists "Manufactura Socpoh")
The concept of community: networking approach. The emergence of the Internet as a new challenge to communities. Sociological professional communities in Russia. Description of the main types of social interactions: commenting and reacting; results.
| Рубрика | Социология и обществознание |
| Вид | дипломная работа |
| Язык | английский |
| Дата добавления | 07.12.2019 |
| Размер файла | 1,4 M |
Отправить свою хорошую работу в базу знаний просто. Используйте форму, расположенную ниже
Студенты, аспиранты, молодые ученые, использующие базу знаний в своей учебе и работе, будут вам очень благодарны.
Yuri Rykov tested the hypothesis about the core-periphery structure in professional communities. This is a structure where nodes in the core are highly linked with each other and peripheral nodes are connected with the core, but no with each other. The core is understood as giant community of the linked nodes inside the network, which have strong connection between each other. The periphery is the group of nodes who are directly connected with the core group and have almost no links between each other. Core-periphery model was purposed by Stephen P. Borgatti and Martin G. Everett in 1999. They reinforced the definitions by three intuitive conceptions to the core-periphery model definition [Borgatti & Everett, 1999: 376]:
1. The idea of a group or network that cannot be subdivided into exclusive cohesive subgroups or factions, although some actors may be much better connected than others.
2. In the terminology of blockmodeling, the core is seen as a 1-block, and the periphery is seen as a 0-block.
3. Nodes that occur near the center of the picture are those that are proximate not only to each other but to all nodes in the network, while nodes that are on the outskirts are relatively close only to the center.
Considering the definition of professional community, it could be claimed that classic sociological definition should not be used, because nowadays the perception of the profession is changing. In each specific context, might be the different approaches to professional identity. In the case of journalists, the real professional practice is more important than profile education, so, the professional community takes for "their colleagues" - people with work experience. Structural characteristic of the professional community is a core-periphery structure, where are the group of strongly interacted with everybody as a core and a group who has some interactions only with core, but ignoring the other groups.
1.5 Sociological professional communities in Russia
There are not so many research about Russian sociological profession or sociological communities. The academic direction of the sociology in Russia was described in historical perspective of biographical interviews with sociologists [Doktorov, 2012], then based on the qualitative interviews with sociologists created network structure of Russian sociologists [Maltseva & Moiseev & Shirokanova & Brik, 2017], case of Zaslavskaya's biography [Maltseva & Moiseev, 2018], St. Peterburg sociology [Sokolov & Bocharov & Guba & Safonova, 2010], bibliographical connectedness of citations, [Batigin & Gradoselskaya, 2001], professional communication in the sociological community [Zadorin & Maltseva, 2013], online sociological community [Zadorin & Maltseva & Polukeev, 2012], case of discussion in the online sociological community [Maltseva, 2016].
Boris Doktorov organized huge project to study generational stratification in the book of 9 volumes among sociologists in historical perspective. In his "ladder" of generations, there are 8 groups: [Doktorov, 2012: Vol.1, 134]
1. 1928-1929 sixties (first-wave)
2. 1929-1934 sixties (second-wave)
3. 1940-1945 wartime
4. 1952-1953 first post-wartime
5. 1964 - 1965 post-warming
6. 1976 - 1977 pre-perestroika (years of stagnation)
7. 1988 - 1989 children of perestroika
8. 2000 - 2002 first generation of post-Soviet Russia
Doktorov published an interview with sociologists of first four generations: Zaslavskaya, Zdravolislov, Lyapin, Shlyapentokh, Yadov, Yelmeev in the first generation, Alekseev, Artemov, Baranov, Gilinki, Maksimov, Rusalinova, Stolovich, Toshenko, Tukumsev, Firsov in the second generation, Belyaev, Bozkov, Voronkov, Gofman, Ionin, Keselman, Konstantinovski, Mogilevski, Panova, Petrenko, Protasenko, Saganenko, Smirnova, Tolstova, Travin, Sheregi in the third generation, Bachinin, Bespalova, Davidov, Zdravolislova, Ille, Ilyin, Kozlova, Myagkov, Semenova, Tarusin,Chirikova, Yadov in the fourth generation. And there are more others interviews in addition to all this generational cohorts.
Using the published interviews by Doktorov, researchers Maltseva, Moiseev, Shirokanova and Brik conducted the network approach based on qualitative biographical interviews. They transformed the data into the one-mode network of actors and two-mode network of actors and affiliations. In order to transform the data, authors performed the coding principles: first, they coded just a part of the data, second, they checked the consistency, refine the instructions and repeat this cycle until the reliability reaches an acceptable level. After achieving sufficient reliability, the entire text array is encoded. [Maltseva & Moiseev & Shirokanova & Brik, 2017].
Using transformed data, Maltseva and Moiseev analyzed ego-network of Tatiana Zaslavskaya. They reviewed the biography of Tatiana Zaslavskaya in the network approach in dynamics - authors highlighted 7 time periods Such mix of qualitative and network approaches allowed the authors to highlight the modality of the relations: "If we turn to the semantic content of the selected networks, then first of all it is necessary to note the different modality of relations: while some people, organizations, events and projects are evaluated positively or neutrally, the evaluation of some of the selected entities is negative." [Maltseva & Moiseev, 2018: 19].
Structure of academic community of sociologists as the subject of the research was demonstrated in the project "St. Petersburg sociology after 1985 year: Institutional dynamics, economic adaptation and points intellectual growth". [Sokolov& Bocharov & Guba & Safonova, 2010] The study was based on a combination of historical, scientometric and sociological approaches, which included such methods as document analysis, interviews, citation analysis. Sociologist's communities from St. Petersburg of 1980-90s were divides into several clusters. Based on 622 individuals they constructed three main segments: West side, East side and Zone-in-Transition. Table 1 is organized to demonstrate the main characteristics of each segment. East side is much more conservative in the settings, much more skeptical in this attitude to "world science".
Table.2 Characteristics of structure of sociologic community in St. Peterburg
|
West side |
East side |
Zone-in-Transition |
||
|
Organizations |
· European University in Saint-Petersburg; · Center for independent sociological research(CNS), · Faculty of sociology in HSE |
· SPBU the faculty of sociology · Research Institute of complex social research-the first research · Institute in the USSR, which was part of the sociological laboratory. |
· Sociological Institute of RAS (faculty cultural studies and sociology of St. Petersburg. Academy of culture and arts). |
|
|
Age and gender |
· Average year birth-1970 · Two-thirds are women. |
· Average year of birth-1958 · Women-49% |
· Average year births-1953 · Predominance of men 60% |
|
|
Number of sociologists |
· 100 sociologists (18.8%) |
· 338 sociologists (62.8%). |
· 99 sociologists (18.4%) |
Another valid research about community of sociologists in Russia was based on the biographical information about early sociologists of 1960-1990s years. The biographical information from the interview was transformed into the egocentric network: "Egocentric networks are an extract of a biographical text, and their informativeness in a certain respect is higher than the informativeness of narratives". [Batigin & Gradoselskaya, 2001, p. 95]. The whole procedure includes 4 steps:
1. Transforming text into egocentric networks
2. Transformation of egocentric networks into a general network of actors.
3. Expansion of the general network of actors.
4. Building the artifact level
However, the interview research material has a number of significant limitations due to the inevitable perception of professional career of the informants, who are influential figures in Russian sociology. Authors confirm the limitations of their research and recognize the possibility of falsification of the obtained data: "Undoubtedly, with a different composition of informants, the content of networks will change, so the question of the representativeness of the results is connected exclusively with the used collection". [Batigin & Gradoselskaya, 2001, p. 89].
The study on professional communication in sociological community was methodically based on mass online survey on the formalized questionnaire [Zadorin & Maltseva, 2013]. One of the research results is that the channels of information exchange gradually give way to new ways of professional communication - communications using the Internet. "With the spread of the Internet, online channels of information and communication are becoming more common" [Zadorin & Maltseva, 2013: 53]. Sociological community in the Internet is also interesting kind of professional community, so Zadorin conducted experimental research of professional cooperation in online community. Regarding the results, author claims that such project is effective: "Professional cooperation and collective implementation they are a joint research project based on a network communication and confidential exchange of resources (knowledge, experience, Finance, technological infrastructure, etc.) turned out to be very effective" [Zadorin, 2012: 165].
There is a research of the online community of sociologists in Facebook ("Manufactura socpoh"), which was conducted by Maltseva, which opens a discussion on a specific case. The same online community "Manufactura Socpoh" database is used in this research. Basing on comments and reactions. Maltseva transformed it into the network with 241 actors. Then, she highlighted 8 clusters, where 6 of clusters are more volumetric, and in 3 of them the degree of "cohesion" (mutual support through likes) in clusters is higher than in the other three. [Maltseva, 2017: 3]
All representations of the structure of the sociologist community in Russia demonstrate academic and professional fields. There is a difference between theorists and practitioners, academics and pollsters, and that these groups are essentially different communities. It is difficult to draw demarcation lines, because in sociology people often work without appropriate education, without degrees - but at the same time they work quite successfully. In general, this is a feature of Russian sociology, because the first sociological department opened in the 1990s, and before that education went to the departments of philosophy. Therefore, a sociologist is not equal to "a person who has received a sociological education". So, we use the approach "from below", where we define man as a sociologist on the accessories (its own category) with the group. And in this case, we study the structure of the online community in group on Facebook. The online community which is the object of this exact research includes the practice sociologists from different state and international organizations.
Taking into account the whole chapter, the definition of online community is a group of people who are involved into the online interactions and share the same social identity. The main feature of online communities is that they provide a possibility for including to routine everyday life such kind of social interactions which existence is impossible in real life. Such community could be considered as community of practice if there are the same area of interest, formed community, the shared practice and technology involvement. The social interactions in the Internet changed into several practices such as posting, sharing, commenting, liking and other. There is a network approach in the research of online social interactions, important groups of nodes that interact with each other in a characteristic way. Regarding the professional community structure, it is a core-periphery, where are the group of strongly interacted with everybody as a core and a group who has some interactions only with core, but ignoring the other groups. As for the sociological professional community, there are studies of professionals from academic and professional fields. However, the structure of online social interactions was not studied. So, in picture 1, based on the literature review, there is the theoretical scheme of the object of the research.
Picture 1. Theoretical scheme of object of the research
2. Methodology
2.1 Research design
The empirical object of this study is professional online group of sociologists from Facebook "Manufactura socpoh". This research is a case study of the structure of member's interactions in online community. This group is very active, contains a large number of participants (2648 actors on 25th of May 2019) from the academic and applied spheres, which was born in 2011.
The subject of sociological research is a structure of social interactions in professional online community of sociologists from Facebook "Manufactura socpoh". Regarding the structure of social interactions, it should be defined as a social differentiation of sets of interactions, which are described from the relations of posting, commenting and expressing the emotional feedback. In terms of social differentiation, the position (role) of the actor is evaluated based on its position in the network, which is measured by certain metrics.
The main aim of the study is to consider the formation of the structure of social interactions in a professional community of sociologists "Manufacture Socpoh" via social network analysis of posts, comments and emotional feedback.
There are several research tasks:
1. To determine the static structure of the professional online community according to commenting practice, reacting practice and combination of these two practices;
2. To classify the actors according to their activity in online behavior based on commenting and reacting in online professional community;
3. To compare the structure of the professional online communities during the period steps.
The main hypotheses of the research are:
· The structure of social interactions of professional online community will have core-periphery form, which means that there will be the highly connected between each other group which is the core group and group with several nodes to the core group.
· The structure of social interactions of professional online community will have core-periphery form, which is stable during the 8 years.
Data description: empirical object of a research
In this research, we study the certain group "Manufactura Socpoh" (Мануфактура "СОЦПОХ". (n.d.). Retrieved from https://www.facebook.com/groups/socpokh/) Ethically, we do not consider it correct to show all the actors, but we specify only the central, public figures, with a large number of posts and comments. And we hope that no one will be offended at us.. The online group was launched on October 1, 2011. The group includes 2648 actors on 25th of May 2019. Sociological discussions are a form of group work on empirical and theoretical material, which does not exclude a news feed and an easy thematic discussion. As in any other facebook group, there are several possible interactions in online group: to write the post, to comment some post, to "give" an emotional feedback such as like, love, thankful and other. In Facebook there are seven possible ways to show up the emotion (Like, Love, Wow, Haha, Sad, Angry, Thankful), however the most popular and used one is "Like".
The data were collected in January 2018 by the usage of official Facebook`s API. Hence the online community was open; it is legally permitted to use public information from the Internet. The base consists of more than 34,000 of posts with the date, authors, number of likes and other emotions. There are 818 authors of these posts, who wrote it during the 2011-2018 years. We organized the data in such a way that the original data were stored in several files. The 1st, 2nd and 3rd dataset column are shown in the Picture 2. Each two columns of these datasets can be seen as two-mode networks. Important characteristic of these datasets is that they are linked - e.g. connected to each other through some common columns (describe). It allows using network multiplication of the obtained 2-mode networks.
Picture 2. The columns of the original dataset
Размещено на http://www.allbest.ru/
To avoid future questions, we note that here are no data from the followers of this community, because of the aim of the research - to construct the structure of the online community based on the communication. These data were stored in .csv file format file.
Picture 3. Post, comment to post, comment to comment structure
As for the structure of the posts and comments in the original data: each post and comment has unique ID. The initial post can be commented, so it will be comment to post, then the comment can be commented, and this comment will be named as comment to comment.
Data preparation
Network of commenting. Out of these data, we constructed the one-mode network of post and comments to posts and comments to comments (PostComm.net), and two-mode network of posts and authors (PostAuthors.net). The PostComm.net was produced by variables (columns) "Post ID" and "Author name", PostAuthors.net was produced out of variables "Post ID" and "Comment ID". To produce these networks, a special program Text2Pajek was used, which transform the column-formed .txt format files to one-mode or two-mode networks (find a link, cite) in .net format, which can be used for the analysis in Pajek [Batagelj, V. (n.d.). How to convert text file datasets into Pajek format. Retrieved from http://vlado.fmf.uni-lj.si/pub/networks/Pajek/howto/text2pajek.htm].
In order to prepare the one-mode network of authors connected to authors by comments relationships (AA_com.net) we multiplied the transposed two-mode PostAuthors.net network (of post ID and author name) with one-mode PostComm.net network (of post ID and comments).
(PA)T * PC = AP * PC = AC
AC * (AC)T = AC * CA = AA(com)
Then we got a one-mode network AA.net with 818 vertices, where the nodes are authors and the links are the connections between them through the relations of their posts commenting. The network is directed from commenting author to posting author. The value of line is equal to the number of times one author commented another author, who are authors of posts and comments to these posts. The titles of the nodes represent the users, and they are left in the same form they are written in Facebook. Then we used Pajek for data analysis [Networks / Pajek . (n.d.). Retrieved from http://vlado.fmf.uni-lj.si/pub/networks/pajek/default.htm].
Network of reacting. From the original dataset, we constructed the one-mode network of reactors (AA_react.net), and one-mode network of authors (Authors.net). The AA_react.net was produced by variables (columns) "Reaction name" and "Author name", Authors.net was produced out of variables "Post ID" and "Author name". The excel function of VLOOKUP was used to combine the Reactor list and Author list using the same Post ID. Then, to produce these networks, a special program Text2Pajek was used to transform in .net format, which can be used for the analysis in Pajek [Batagelj, V. (n.d.). How to convert text file datasets into Pajek format. Retrieved from http://vlado.fmf.uni-lj.si/pub/networks/Pajek/howto/text2pajek.htm].
Then we got a one-mode network with 1539 vertices, where the nodes are authors and the links are the connections between them through the relations of their posts reacting. The network is directed from reacting actor to posting author. The value of line is equal to the number of times one author commented another author, who are authors of posts and reactions to these posts. The titles of the nodes represent the users, and they are left in the same form they are written in Facebook. Then we used Pajek for data analysis [Networks / Pajek . (n.d.). Retrieved from http://vlado.fmf.uni-lj.si/pub/networks/pajek/default.htm].
Combined network based on commenting and reacting. Two one-mode node networks based on comments and likes are combined using Python. As a result, we have a multirelational network on the activity including comments and likes. Such network composed of two or more sets of edges between a set of vertices, in this cases there are two sets of edges: commenting and reacting.
Dynamical steps for the dynamic networks. Regarding the dynamic network analysis, we add to the static network to temporal mode, which was separated to each month of the communication since September of 2011. First, we prepared the network based on comments (AA_com.net) and network based on reactions (AA_react.net) with the temporal mode. There are two ways to convert it to temporal type: one is to divide into parts by hands and create networks which are needed. The second way to deal with it is the temporal quantities approach. So, we using the first approach in dynamical analysis, but the second approach will be implemented later. In order to make this temporal mode, we transformed the data format with year, month, day and time to the time period with each month step, by using the formula: 12*(year-2011) + month. We subtract the year 2011, because this year when the online community was born. So, we got periods 1-54, which allows us to study the changes in the network by month. Then, we need to transform the data into network by using Text2Pajek [Batagelj, V. (n.d.). How to convert text file datasets into Pajek format. Retrieved from http://vlado.fmf.uni-lj.si/pub/networks/Pajek/howto/text2pajek.htm]. After that, we have the file with the .paj format, but we need to do several manipulations to convert it to temporal form, such as put in brackets the time variable [time period] and add to all vertices [1-*].
2.2 Description of the main types of social interactions: commenting and reacting
Regarding the post writing and commenting activity during 8 years (picture 4), there are two main periods with increased activity: period from January 2015 till November 2015 and period from December 2015 till May 2016. So, such periods will be explained in order to show how such activity influenced to the social structure.
Picture 4.Post writing and commenting in e
ach month during 8 years
The activity can be seen as the number of posts, comments and comments to comments altogether. At the table 3 there is a distribution of activity during 8 years, but data from 2018 exists until February. The possibility to give a comment to another comment starts in 2015 year. There is a rise in post commenting in 2014 and 2015 years. In 2016 year the number of comments to comments is close to 6 thousand.
Table 3. Posts, types of comments during the 8 years
|
Years |
Posts |
Comments to posts |
Comments to comments |
|
|
2011 |
189 |
1035 |
0 |
|
|
2012 |
479 |
2803 |
0 |
|
|
2013 |
386 |
2860 |
0 |
|
|
2014 |
416 |
4432 |
0 |
|
|
2015 |
367 |
5590 |
387 |
|
|
2016 |
431 |
2406 |
5954 |
|
|
2017 |
276 |
1367 |
3936 |
|
|
2018 |
47 |
216 |
728 |
The activity can be seen as the number of posts, comments and comments to comments altogether. There are the top of 20 most active users in writing posts, commenting the posts and commenting the comments (Figure 1). This includes the number of posts, comments and comments to comments altogether. The most active user is Ivan Nizgoraev with 7471 points. Next, Victor Korb took 2nd place with 1590 result. The difference between "1st" and "2nd" overall activity places is 5881. Alexandr Semeonov has the minimum of top 20 - 297 points. The mean of the overall activity is 41,9, but without the leader of the group, it is 32,8.
Figure 1. The overall activity distribution in the networks
In the table 4., there is a distribution of each part of overall activity in the group: number of posts, number of comments, number of comment to comment, maximum number of "like" and maximum number of comment. Ivan Nizgoraev is the leader of number of posts in group and Alexey Alexeev took 2nd place with 377 and 193 points respectively. The third place took Trilena Koneva - 161 points which is almost 2 times less than Ivan Nizgoraev.
Regarding the comments, the most active is Ivan Nizgoraev with 3959 comments, and 4 times less result has Victor Korb - 908 comments. On the 3rd place Violetta Khabibulila with almost the same result - 868. As for the comments to comments, with 5 times result difference Ivan Nizgoraev has 3135 comments to comments and Victor Korb only 620. Next, Elena Chernova commented 271 times other comments. The maximal number of likes has Ivan Nizgoraev with 89 likes, then Валерий Фёдоров has only 6 likes less - 83.As for the maximal number of comments: the leader is Ivan Nizrogaev and he has 345 comments under his post. Here is Timur Osmanov shown up with 329 comments and Victor Korb had 304 comments and took 3rd place.
Table 4. General statistics distribution
|
Name |
N of Posts |
N of Comments |
N of Comments to comments |
Max "likes" |
Max number of comments |
|
|
Ivan Nizgoraev |
337 |
3959 |
3135 |
89 |
345 |
|
|
Alexey Alexeev |
193 |
277 |
202 |
46 |
38 |
|
|
Trilena Koneva |
161 |
452 |
197 |
53 |
88 |
|
|
Владимир Звоновский |
87 |
408 |
70 |
79 |
173 |
|
|
Igor Zadorin |
83 |
666 |
138 |
60 |
193 |
|
|
Victor Korb |
62 |
908 |
620 |
36 |
304 |
|
|
Violetta Khabibulina |
44 |
868 |
51 |
21 |
61 |
|
|
Елена Князева |
41 |
317 |
155 |
21 |
50 |
|
|
Alexei Titkov |
24 |
297 |
39 |
39 |
201 |
|
|
андрей игнатьев |
20 |
709 |
126 |
16 |
67 |
|
|
Дмитрий Фролов |
17 |
200 |
145 |
17 |
26 |
|
|
Timur Osmanov |
14 |
312 |
159 |
79 |
329 |
|
|
Nikita Krylnikov |
13 |
146 |
229 |
15 |
15 |
|
|
Alexander Semeonov |
13 |
250 |
34 |
27 |
44 |
|
|
Виктор Гребенников |
12 |
235 |
54 |
5 |
109 |
|
|
Anton Karpov |
10 |
814 |
0 |
7 |
34 |
|
|
Elena Chernova |
6 |
107 |
271 |
14 |
87 |
|
|
Валерий Фёдоров |
6 |
286 |
45 |
83 |
74 |
|
|
Maxim Tikhonov |
5 |
119 |
209 |
30 |
26 |
|
|
Elena Grach |
4 |
296 |
198 |
19 |
20 |
In the figure 2, there is a distribution of the most "liked" posts is presented from the 2011 till 2018 years. The trend was growing until the 2013 year, and stayed on the same level. As for the topic of the most "liked" posts, there is job vacancy information, descriptive opinion about some new article or book. Also, the same posts in 2014 and 2015 became the most commented and "graded" the most number of likes. These posts are about the research cooperation of FOM and WCIOM and about the video about object and subject sociological of the research from the Postnauka service. These posts also have the highest number of comments. There are topics such as educational process in universities, methodological issues of sociological science and criticism of published articles.
Figure 2. Max likes and comments during the 8 years
To sum up, the activity increasing in the 2014 and 2015 years in comments and in likes. These periods will be displayed in the dynamic analysis of structure of online professional community. Regarding the network members, there are the most active participants in social interaction, such as Ivan Nizgoraev, Alexey Alexeev, Trilena Koneva, Владимир Звоновский, Igor Zadorin, Victor Korb, Violetta Khabibulina.
Social network analysis as a research methodology
Social networks represent relationships among actors. This section of mathematics offers its tools for analysis and search of data in the online space. In order to understand how was formed the professional online community, what structural characteristics and patterns of interactions were changed; it is important to use the specific method to recognize the dynamic of the structure. "Network analysis enters into the process of model development, specification, and testing in a number of ways: it express relationally defined theoretical concepts but providing formal definitions, measures and descriptions, to evaluate models and theories in which key concepts and propositions are expressed as relational processes or structural outcomes, or to provide analyses of multirelational systems". [Wasserman & Faust, 5] Such method traced back to sociometry of Y. Moreno, where was aimed to display the dynamics of some group, - how is structured a network of relationships in group. "Sociometry opened up a new possibility of genuine planning of human society for the reason that the factors of spontaneity, the initiative and the momentary grasp of the individuals concerned were made the essence of the method of exploration and of the investigation itself". [Moreno, 1941: 17]
In Maltseva dissertation, there are 4 periods of formation of social network analysis as a discipline by Freeman, but Maltseva complements these periods, so there are 6 periods.
· 1st period: (mid.19th century- 1920s) background to social network analysis: classic sociologists used the term network as metaphor
· 2nd period: (1930-40s) "birth and death" network analysis: development of research in anthropology, social psychology
· 3rd period: (1940-60s) "dark ages": "The name reflects the idea that network analysis was developed by groups of scientists from different disciplines, not interacting with each other" [Maltseva, 2014: 35].
· 4th period: (1950-60s) structural network analysis: development of the metaphor of the network in ethnographic researches and usage of graph mathematical theory in the quantitative approach
· 5th period: (1970s) "growing up" network analysis: Harrison White influence, eclectic and interdisciplinary community of studies in network approach
· 6th period: (1990s) critic of the network analysis
So, such dynamics of the development of the social network approach is commented as: "A review of the formation of structural analysis of social networks shows that over time, the concept of the network, used as a metaphor, analytical tool, has developed into a broader perspective, including a specific view of the social structure and ways to measure it." [Maltseva, 2014: 40].
As the method of the analysis, social network analysis was chosen, as it could help to observe the structure of the online community and the patterns of interaction. Instead of asking questions about different types of interactions, it is possible to see what types of interactions hold together a particular network. The idea of relational tie is that "actors are linked to one another by social ties". [Wasserman& Faust, 18] In this study authors of the posts are linked with each other by comments and "giving" emotional feedback: like, love, and other. Connection between actors also could provide the information about the direction of such link. For example, in this case we analyze directed ties, in order that we know which exact actor "gave" a like to exact post or who comment on exact post. The analyzing object also could be undirected ties, where the direction of the tie does not play a key role. For instance, online friendship: there is no direction, because it is pointless. Another important term in social network analysis is mode. "A distinct set of entities which the structural variables are measured". [Wasserman& Faust, 29] One-mode networks are measured on a single set of actors, while two-mode networks contain two sets of actors. The network of posts and comments is one -mode network, while network of actors, who comments each other's posts is one-mode network.
Regarding the dynamic analysis of social network, it is possible to detect the community structure in process of formation - to identify the development of connected groups of interaction inside the frame of online communication. Some researchers of the evolution of communities provide various insights and opportunities of the dynamic social network: 1) understanding the structures of the complex networks; 2) detecting a drastic change in the interaction patterns; 3) making predictions on the future trends of the network, etc. [Takaffoli & Sandi & Fagnan & Zaiane, 2011, 49].
For the network analysis were used such programs as Pajek, WoS2Pajek, R, Python. Pajek is a program created by Vlado Batagel for analyses and visualization of large networks. [Networks / Pajek . (n.d.). Retrieved from http://vlado.fmf.uni-lj.si/pub/networks/pajek/default.htm] Txt2Pajek is additional program created by Batagel, which transforms the text format data into the paj. format in order to use it in Pajek. [Batagelj, V. (n.d.). How to convert text file datasets into Pajek format. Retrieved from http://vlado.fmf.uni-lj.si/pub/networks/Pajek/howto/text2pajek.htm]
Basic terms of social network analysis. The nodes are the entities in graph. In this research the nodes are the members of community. The edge defined as relationships between nodes. If we consider online community as a graph then every comment or reaction is an edge. [Aks. (1970, January 01). Introduction to Network Analysis terminology. Retrieved from https://datavu.blogspot.com/2013/10/sna-social-network-analysis-basic.html] Loops are lines, which are connected with themselves. Directed graph could be defined as the relationship may not be valid in both directions (connecting nodes) then it is called directed graph. For example, Ivan gives reaction to Igor, but Igor does not dive reaction to Ivan. Density defines as the number of the potential links in a network that are actual links.
As for the centrality metrics, there are several measurements such as degree centrality, betweenness centrality, closeness centrality. [Aks. (1970, January 01). Introduction to Network Analysis terminology. Retrieved from https://datavu.blogspot.com/2013/10/sna-social-network-analysis-basic.html]. Degree Centrality is defined as number of edges connected with a particular node defines the degree centrality of the node. Betweenness centrality is a number shortest paths between all nodes in a network which pass through a given node. Closeness centrality is a length of average shortest path between a given node and all other nodes in a graph.
In our analysis, we use line cut, islands approach, strong component analysis, cliques. As for the line cut definition there is one: "A line cut is a set of l lines that, it deleted, disconnects the graph" [Wasserman & Faust, 1994: 114]. Islands in the net are defined "as a connected small subnetwork of size in the interval k .. K with stronger internal cohesion relatively to its neighborhood". [Nooy W. & Mrvar A. & Batagelj V, 2011] As for the network structure analysis, the strong component analysis and cliques analysis is used. Nooy, Mrvar and Batagelj define a strong component as "a maximal strongly connected subnetwork". As for the clique, Wasserman and Faust provide their definition "a clique in a graph is maximal complete subgraph of three or more nodes".
3. Research results
3.1 Static analysis of the networks based on comments and reactions
Basic statistics of the networks
The table 5 has the information about networks (AA_com.net) based on the relation of comment and the network (AA_react.net) based on reactions.
Table 5. The static networks based on comments (AA_com.net) and on reactions (Reaction.net)
|
Network name |
N Actors |
N Edges |
N Loops |
Density |
|
|
AA_com.net |
818 |
5536 |
353 |
0.0083 |
|
|
RA_react.net |
1539 |
43991 |
128 |
0.0186 |
The network AA_com.net based on the relation of the post and comment, so the nodes represent authors who comment each other's posts and comments. Degree centrality measures the popularity of activity of each actor in the graph. The graph is directed, so there are indegree and outdegree centrality. Indegree represents the number of edges incoming to a vertex, and outdegree represents the number of edges outgoing from a vertex. In this case, indegree of certain node show up the number of comments that the author got for all her or his posts. Outdegree of the certain node shows the number of comments, which author gave to others. Degree is a sum of indegree and outdegree. In Table 6.1., there is a list of the most active and popular authors in terms of degree, indegree and outdegree centralities. Posts of Ivan Nizgoraev are the most commented and as actor he is the most active and popular one.
Table 6.1. The most popular(comment and commented) authors in terms of receiving comments (indegree) and giving comments (outdegree)
|
N |
Name |
Degree |
Indegree |
Outdegree |
|
|
1 |
Ivan Nizgoraev |
712 |
361 |
351 |
|
|
2 |
Igor Zadorin |
248 |
155 |
93 |
|
|
3 |
Victor Korb |
248 |
124 |
124 |
|
|
4 |
Владимир Звоновский |
215 |
149 |
39 |
|
|
5 |
Alexey Alexeev |
203 |
125 |
78 |
|
|
6 |
Trilena Koneva |
182 |
132 |
70 |
|
|
7 |
Violetta Khabibulina |
171 |
59 |
112 |
|
|
8 |
Timur Osmanov |
161 |
91 |
70 |
|
|
9 |
Елена Князева |
152 |
84 |
68 |
|
|
10 |
Larisa Pautova |
143 |
96 |
47 |
|
|
11 |
Дмитрий Фролов |
105 |
50 |
55 |
|
|
12 |
Nikita Krylnikov |
102 |
39 |
63 |
|
|
13 |
Maxim Tikhonov |
100 |
47 |
53 |
|
|
14 |
Elena Grach |
96 |
30 |
66 |
|
|
15 |
Evgeni Varshaver |
94 |
59 |
35 |
|
|
16 |
Elena Chernova |
93 |
52 |
41 |
|
|
17 |
Anton Karpov |
92 |
15 |
77 |
|
|
18 |
Валерий Фёдоров |
92 |
49 |
43 |
|
|
19 |
Alexei Titkov |
91 |
57 |
34 |
|
|
20 |
Alexander Semeonov |
87 |
42 |
45 |
As for the network based on reaction, the most popular and active actor is the same - Ivan Nizgoraev (Table 6.2.). Numbers of the values are much larger; hence to give a reaction is much easier than give a comment. In terms of indegree, in receiving the reaction, seems like the most active users are more likely to receive the reaction, than to give a reaction. However, there are some actors whose reacting activity is symmetrical in terms of indegree and outdegree reacting.
Table 6.2. The most popular(who are reacted by someone and who give a reaction) actors in terms of receiving reaction (indegree) and giving reaction (outdegree)
|
N |
Name |
Degree |
Indegree |
Outdegree |
|
|
1 |
Ivan Nizgoraev |
1300 |
816 |
484 |
|
|
2 |
Владимир Звоновский |
599 |
409 |
190 |
|
|
3 |
Igor Zadorin |
595 |
433 |
162 |
|
|
4 |
Trilena Koneva |
579 |
463 |
116 |
|
|
5 |
Victor Korb |
466 |
302 |
164 |
|
|
6 |
Timur Osmanov |
392 |
220 |
172 |
|
|
7 |
Alexey Alexeev |
386 |
371 |
15 |
|
|
8 |
Larisa Pautova |
383 |
272 |
111 |
|
|
9 |
Анна Кулешова |
380 |
315 |
65 |
|
|
10 |
Violetta Khabibulina |
349 |
148 |
201 |
|
|
11 |
Nikita Krylnikov |
331 |
131 |
200 |
|
|
12 |
Елена Князева |
324 |
216 |
108 |
|
|
13 |
Валерий Федоров |
292 |
157 |
135 |
|
|
14 |
Мария Мацкевич |
258 |
169 |
89 |
|
|
15 |
Anton Karpov |
245 |
99 |
146 |
|
|
16 |
Elena Grach |
235 |
98 |
137 |
|
|
17 |
Liudmila Presnyakova |
235 |
166 |
69 |
|
|
18 |
Denis Strebkov |
234 |
200 |
34 |
|
|
19 |
Alexei Titkov |
215 |
158 |
57 |
|
|
20 |
Петр Залесский |
208 |
165 |
43 |
Weighted degree is a measure similar to the degree, but it is based on the number of edges for a node, pondered by the weight of each edge. Weighted degree calculates the sum of the weights of the edges and can be also counted as incoming and outgoing ties. And counts all the comments which some person give to other one. There is still the same leader, but the second place was changed: Victor Korb is more popular than Igor Zadorin, because people who comments Igor Zadorin's posts have less weight than Victor's commenters. Trilena Koneva and Владимир Звоновский commented more popular actors in this network of communication. (Table 7.1.).
Table 7.1. The most popular (comment and commented) authors (weighted links)
|
N |
Name |
Weighted degree |
Weighted indegree |
Weighted outdegree |
|
|
1 |
Ivan Nizgoraev |
11509 |
6652 |
7094 |
|
|
2 |
Victor Korb |
2619 |
1455 |
1528 |
|
|
3 |
Igor Zadorin |
2104 |
1545 |
804 |
|
|
4 |
Trilena Koneva |
1741 |
1420 |
649 |
|
|
5 |
Владимир Звоновский |
1536 |
1239 |
478 |
|
|
6 |
Alexey Alexeev |
1249 |
937 |
479 |
|
|
7 |
Timur Osmanov |
1195 |
844 |
471 |
|
|
8 |
Violetta Khabibulina |
1114 |
296 |
919 |
|
|
9 |
андрей игнатьев |
1032 |
346 |
835 |
|
|
10 |
Елена Князева |
889 |
504 |
472 |
|
|
11 |
Anton Karpov |
871 |
57 |
814 |
|
|
12 |
Alexei Titkov |
800 |
596 |
336 |
|
|
13 |
Elena Chernova |
694 |
469 |
378 |
|
|
14 |
Elena Grach |
623 |
129 |
494 |
|
|
15 |
Валерий Фёдоров |
579 |
292 |
331 |
|
|
16 |
Nikita Krylnikov |
533 |
158 |
375 |
|
|
17 |
Дмитрий Фролов |
529 |
267 |
345 |
|
|
18 |
Larisa Pautova |
507 |
359 |
148 |
|
|
19 |
Maxim Tikhonov |
505 |
177 |
328 |
|
|
20 |
Виктор Гребенников |
486 |
277 |
289 |
As for the network based on reaction, the leader is the same Ivan Nizgoraev (table 7.2.). The second and third places take Igor Zadorin and Владимир Звоновский, but Igor Zadorin is less likely to give the reaction, he is more often receiving reactions from others.
Table 7.2. The most popular (react and reacted) actors (weighted nodes)
|
N |
Name |
Weighted degree |
Weighted indegree |
Weighted outdegree |
|
|
1 |
Ivan Nizgoraev |
12202 |
7887 |
4330 |
|
|
2 |
Igor Zadorin |
2869 |
2229 |
648 |
|
|
3 |
Владимир Звоновский |
2719 |
1705 |
1018 |
|
|
4 |
Trilena Koneva |
2486 |
2082 |
409 |
|
|
5 |
Victor Korb |
2342 |
1282 |
1063 |
|
|
6 |
Timur Osmanov |
1486 |
710 |
776 |
|
|
7 |
Violetta Khabibulina |
1363 |
456 |
914 |
|
|
8 |
Anton Karpov |
1315 |
536 |
779 |
|
|
9 |
Елена Князева |
1284 |
808 |
476 |
|
|
10 |
Larisa Pautova |
1238 |
813 |
430 |
|
|
11 |
Валерий Фёдоров |
1195 |
478 |
717 |
|
|
12 |
Nikita Krylnikov |
1166 |
596 |
929 |
|
|
13 |
Alexey Alexeev |
1054 |
1029 |
880 |
|
|
14 |
Анна Кулешова |
996 |
853 |
143 |
|
|
15 |
Гульшат Уразалиева |
976 |
103 |
880 |
|
|
16 |
андрей игнатьев |
875 |
643 |
232 |
|
|
17 |
Мария Мацкевич |
751 |
439 |
345 |
|
|
18 |
Elena Grach |
731 |
238 |
494 |
|
|
19 |
Alexey Kuzmin |
649 |
140 |
509 |
|
|
20 |
Nadezhda Korytnikova |
611 |
65 |
546 |
Closeness centrality of the node indicates how long it will take for information from a given node to reach other nodes in the network (table 8.1.) Ivan Nizgoraev, Igor Zadorin, Victor Korb, Владимир Звоновский, Alexey Alexeev and Trilena Koneva also have leader's positions in the network. The betweenness centrality is more interesting, because it is an indicator of importance in the network. It is described as the number of shortest paths from all the vertices to all the other vertices in the network that pass through the node in consideration. The most powerful actor is Ivan Nizgoraev, and others have lower indicators of betweenness centrality.
Table 8.1. The most informed and important authors
|
N |
Name |
Closeness all |
Closeness in |
Closeness out |
Betweenness |
|
|
1 |
Ivan Nizgoraev |
0,68 |
0,57 |
0,46 |
0,31 |
|
|
2 |
Igor Zadorin |
0,52 |
0,46 |
0,32 |
0,03 |
|
|
3 |
Victor Korb |
0,52 |
0,45 |
0,34 |
0,03 |
|
|
4 |
Владимир Звоновский |
0,51 |
0,46 |
0,30 |
0,03 |
|
| ... |
Подобные документы
Social structure as one of the main regulators of social dynamic. The structure of the social system: social communities, social institutions, social groups, social organizations. The structure of social space. The subsystem of society by T. Parsons.
презентация [548,2 K], добавлен 06.02.2014The essence of social research communities and their development and functioning. Basic social theory of the XIX century. The main idea of Spencer. The index measuring inequality in income distribution Pareto. The principle of social action for Weber.
реферат [32,5 K], добавлен 09.12.2008The concept, definition, typology, characteristics of social institute. The functions of social institution: overt and latent. The main institution of society: structural elements. Social institutions of policy, economy, science and education, religion.
курсовая работа [22,2 K], добавлен 21.04.2014The need for human society in the social security. Guarantee of social security in old age, in case of an illness full or partial disability, loss of the supporter, and also in other cases provided by the law. Role of social provision in social work.
презентация [824,4 K], добавлен 16.10.2013Understanding of social stratification and social inequality. Scientific conceptions of stratification of the society. An aggregated socio-economic status. Stratification and types of stratification profile. Social stratification of modern society.
реферат [26,9 K], добавлен 05.01.2009Four common social classes. Karl Marx's social theory of class. Analysis the nature of class relations. The conflict as the key driving force of history and the main determinant of social trajectories. Today’s social classes. Postindustrial societies.
презентация [718,4 K], добавлен 05.04.2014The essence of the terms "Company" and "State" from a sociological point of view. Description criteria for the political independence of citizens. Overview of the types of human society. The essence of the basic theories on the origin of society.
реферат [20,1 K], добавлен 15.12.2008The essence of modern social sciences. Chicago sociological school and its principal researchers. The basic principle of structural functionalism and functional imperatives. Features of the evolution of subprocesses. Sociological positivism Sorokina.
реферат [34,8 K], добавлен 09.12.2008American marriage pattern, its types, statistics and trends among different social groups and ages. The reasons of marriage and divorce and analyzing the statistics of divorce and it’s impact on people. The position of children in American family.
курсовая работа [48,3 K], добавлен 23.08.2013Overpopulation, pollution, Global Warming, Stupidity, Obesity, Habitat Destruction, Species Extinction, Religion. The influence of unemployment in America on the economy. The interaction of society with other societies, the emergence of global problems.
реферат [21,1 K], добавлен 19.04.2013Description situation of the drugs in the world. Factors and tendencies of development of drugs business. Analysis kinds of drugs, their stages of manufacture and territory of sale. Interrelation of drugs business with other global problems of mankind.
курсовая работа [38,9 K], добавлен 13.09.2010Problems in school and with parents. Friendship and love. Education as a great figure in our society. The structure of employed young people in Russia. Taking drugs and smoking as the first serious and actual problem. Informal movements or subcultures.
контрольная работа [178,7 K], добавлен 31.08.2014The study of human populations. Demographic prognoses. The contemplation about future social developments. The population increase. Life expectancy. The international migration. The return migration of highly skilled workers to their home countries.
реферат [20,6 K], добавлен 24.07.2014The nature and content of the concept of "migration". The main causes and consequences of migration processes in the modern world. Countries to which most people are emigrating from around the world. TThe conditions for obtaining the status of "migrant".
презентация [4,8 M], добавлен 22.03.2015История развития сети "Internet" как всемирной системы объединенных компьютерных сетей. Определение социальной значимости поисковых, почтовых и справочных ресурсов сети "Internet". Место социальных сетей в информационной жизни украинского общества.
реферат [31,2 K], добавлен 04.07.2015The concept and sex, and especially his studies in psychology and sociology at the present stage. The history of the study of the concepts of masculinity and femininity. Gender issues in Russian society. Gender identity and the role of women in America.
дипломная работа [73,0 K], добавлен 11.11.2013Развитие социологии. Изучение и обладание навыками использования Интернет ресурсами. Интернет как источник информации. Интернет-опросы - новая техника работы. Сетевые социологические исследования. Технологии организации и проведения сетевых исследований.
контрольная работа [32,3 K], добавлен 25.11.2008The concept of public: from ancient times to era of Web 2.0. Global public communication. "Charlie Hebdo" case. Transition of public from on-line to off-line. Case study: from blog to political party. "M5S Public": features and mechanisms of transition.
дипломная работа [2,7 M], добавлен 23.10.2016The subjective aspects of social life. Social process – those activities, actions, operations that involve the interaction between people. Societal interaction – indirect interaction bearing on the level of community and society. Modern conflict theory.
реферат [18,5 K], добавлен 18.01.2009Formation of a religious community living together. The impact of the formation of the community of practice in modern conditions in the context of Community Baptist. Humility as a guide path, forming relationships and types of activity of the commune.
автореферат [54,5 K], добавлен 26.11.2014
