Encyclopedia of Social Network Analysis and Mining

Living Edition
| Editors: Reda Alhajj, Jon Rokne

Political Networks

  • Stefan StieglitzEmail author
Living reference work entry
DOI: https://doi.org/10.1007/978-1-4614-7163-9_86-1

Keywords

Social Medium Social Network Analysis Social Network Site Political Institution Sentiment Analysis 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Synonyms

Glossary

E-participation

The process by which public concerns, needs, and values are incorporated into governmental and corporate decision-making.

Politics 2.0

The harnessing of the Internet’s lowered transaction costs and its condition of information abundance, toward the goal of building more participatory, interactive political institutions.

Social media analytics

Its primary goal is to develop and evaluate scientific methods as well as technical frameworks and software tools for tracking, modeling, analyzing, and mining large-scale social media data for various purposes.

Definition

Political networks are social aggregations where citizens and politicians are connected and share and discuss ideas on political processes.

Introduction

In democratic countries, political parties feel responsible to participate in public political discussion. Following traditional theories, politicians and journalists bring up and moderate those political discourses as “agenda setter.” However, the traditional structure of mass communication in the political context has been changed by the upcoming of social media (Stieglitz et al. 2014; Chadwick 2006). Based on the rapid development of Web 2.0 technologies and associated social media, Internet users are now enabled to create public content on their own. Recently, social media such as blogs, Facebook, and Twitter have shown a strong growth of users (Stieglitz et al. 2014; Wigand et al. 2010; McAfee 2006). This development affects political discussions and supports the creation of new forms of political networks in the Internet. A main driver for this is a change of the physics of information diffusion caused by a massive reduction of cost of the technical infrastructure required to reach a large number of people (Stieglitz and Dang-Xuan 2012). Based on services such as microblogging, social network sites (SNS), and weblogs, individuals (e.g., citizens) and political institutions (e.g., politicians, political parties, political foundations, and think tanks) are enabled to follow or participate into public political discussions (Dang-Xuan and Stieglitz 2012). As one of the most prominent examples, Barack Obama successfully managed to utilize social media to attract voters within his last election campaign (Wattal et al. 2010). Therefore, providing information via social media and interacting with voters in social media becomes an important way to establish and extend political networks.

From the point of political actors, it is necessary to better understand the dynamics of political discussions in social media in order to participate and establish political networks among their potential voters. This is, however, a challenging task because of the sheer amount of data, the unstructured content, and the changing roles of individuals within the discussions. Additionally, it has to be considered that social media services provide a variety of different functionalities such as the “Like” button and most recently other emotional expressions on Facebook and the limitation of message length on Twitter which also have an influence on communication in social media. Therefore, from the perspective of political institutions and government agencies, there is a need to continuously gather, monitor, analyze, summarize, and visualize politically relevant information from online social media with the goal to improve communication with citizens and voters (e.g., Zeng et al. 2010; Kavanaugh et al. 2011; Paris and Wan 2011; Stieglitz and Brockmann 2013; Stieglitz and Dang-Xuan 2013).

In contrast to communication in traditional media, it is more difficult to identify opinion makers in political networks which are based on social media. Another challenge might be to identify emerging topics and issues as early as possible or even to analyze social media data to predict developments. In this sense, social media analytics is supposed to provide tools and concepts to collect, monitor, analyze, summarize, and visualize social media data in an automated way due to the massive amount of (mostly unstructured) social media data (Zeng et al. 2010). Approaches of social media analytics have not only been applied in the political context and government sector (e.g., Kavanaugh et al. 2011; Paris and Wan 2011) but also in the context of business and marketing (e.g., Gruhl et al. 2010; Larson and Watson 2011).

Key Points

Social media are understood as “a group of internet-based applications that build on the ideological and technological foundations of Web 2.0 that allow the creation and exchange of usergenerated content” (Kaplan and Haenlein 2010). Besides traditional ways of building political networks (campaigns, events, speeches), social media such as SNS, weblogs, microblogging, and wikis now play an increasingly important role in shaping political communication in the USA and around the world (e.g., Aday et al. 2010; Tumasjan et al. 2011). As Stieglitz and Dang-Xuan (2012) state, the potentials of social media appear to be most promising in political context as they can be an enabler for more participation and democracy. “E-participation” focuses not only on this process but also on using the Internet as an additional or exclusive instrument to support building of political networks, e.g., by creating dialogues between the elected and the electorate. In this context, Karpf (2009) introduced the term of “politics 2.0,” which can be understood as the harnessing of the Internet’s lowered transaction costs and its condition of information abundance, toward the goal of building more participatory, interactive political institutions.

There is a growing body of research focusing on the role of social media in political deliberation. The US presidential campaign in 2008 and also the following elections in the USA have shown that social media technologies have become increasingly important for political communication and persuasion (Wattal et al. 2010). It became obvious that social media could be successfully adapted to contact and discuss with voters as well as to disseminate important information to them. Especially young people were inspired to political topics by using social media as communication platform (Chen et al. 2009; Kushin and Kitchener 2009). Based on this new form of political discussion, numerous people are enabled to participate and connect among each other building political networks. Studies show that a growing number of people on Facebook, Twitter, or in blogs are getting involved into political communication (Meth et al. 2015). While political networks on Twitter seem to be based mainly on information sharing rather than on discussions, Facebook and blogs often support discursive discussions among citizens. However, it has to be considered that communication on Facebook takes place between friends and therefore is not public generally. In contrast to this, Twitter allows people to discuss among each other publicly.

Historical Background

Individuals started to use computer-mediated communication early for political online discussions (e.g., by using bulletin boards, chat rooms). By the upcoming of social media, the relevance of political networks in the Internet grew. Platforms such as Facebook or YouTube are used as spaces to share political content (e.g., in the US election campaign of Barack Obama). Moreover, there still exist many discussion boards, political platforms, and online groups that are used for ongoing political discussions.

Analysis and Mining on Political Networks

Based on massive changes in the public communication among citizens, it becomes obvious that political institutions as well as government services need to leverage social media resources to improve services and communication with citizens and voters (e.g., Kavanaugh et al. 2011; Paris and Wan 2011). In order to react and participate, politicians need to stay updated about current discussions and to manage one’s own reputation in virtual communities, particularly regarding emerging topics that can end up in a scandal or crisis for a specific politician or party (e.g., Zeng et al. 2010; Stieglitz et al. 2012).

In an exploratory study of social media use by government officials in Virginia, USA, Kavanaugh et al. (2011) find that social media aggregation tools are needed to make sense of the overwhelming amount of data that is being generated, to model the flow of information, and to identify patterns over time. There might be already some tools for these purposes; however, these tools are designed to support businesses and not government, so they are not optimal for civic needs. Moreover, government officials would prefer digital libraries to archive and curate user-generated content, especially for crisis and social convergence situations but also for analyses that cover longer time frames.

Three main reasons for social media monitoring activities for government services are measuring campaign effectiveness, measuring the impact of and reaction to content produced by the institution, and offering improved services by interacting with the online community regarding specific social media postings. Many politicians by using social media also attempt to look for feedback, suggestions, and new ideas from their group members, followers, and others for their political work. However, this is a task that requires much effort, appropriate tools, and particularly a systematic approach, which most politicians cannot afford due to limited resources and capacities.

From the point of political institutions, there exist different approaches of data tracking which depend on the specific intentions of that institution: (1) self-involved, (2) keyword/topic based, (3) actor based, (4) random/exploratory, and (5) URL based (Bruns and Stieglitz 2014; Stieglitz and Dang-Xuan 2012).

Self-Involved Approach

The first approach is applicable when, for example, individual politicians or political parties want to find out explicitly how people are talking about them in social media. In such case, the politicians or parties can have all tweets collected that contain their name as either simple keyword or hashtag (#). If they have an own Facebook presence in terms of a page or group, they should track all posts and corresponding comments published by users or fans/members of their own page or group. Likewise, if they also have an own blog, all comments to their blog entries should be gathered for analysis. Furthermore, it might be useful to collect all Facebook and blog postings that contain their name from external predefined Facebook groups/pages and blogs, respectively.

Keyword-Topic-Based Approach

Political actors are usually highly interested in the feedback or opinions of social media users to certain political topics. Here, the second tracking approach seems to be eligible where tweets as well as Facebook and blog postings that involve keywords related to topics of interest can be tracked. To attain a high level of data completeness, relevant keywords representing the topic of interest have to be carefully and systematically chosen in advance. The broader the topic to be analyzed, the more keywords should be taken into account.

Actor-Based Approach

In political communication, particularly in the blogosphere and recently on SNS and microblogging platform, there are usually a number of actors who can be considered as more influential or more popular than most other users. These actors are said to have the power to influence (online) opinion-making processes. Therefore, politicians or political parties are also interested in monitoring such important users in terms of their generated content. For that, an actor-based tracking approach might be employed to track tweets, wall postings, and blog entries as well as corresponding comments specifically contributed by those influential users who should also be identified in advance.

Random/Exploratory Approach

Contrary to the first three tracking approaches which are rather of targeted nature, the fourth approach supports exploratory inductive content mining. The idea behind this tracking approach is to randomly select one or several sets of data (tweets, Facebook, or blog postings) for different time periods for analysis. Based on these random data sets, particularly content analysis might be conducted to identify major political topics and detect users’ opinions or sentiment associated with those topics.

URL-Based Approach

Given that social media platforms are widely used, among other purposes, to disseminate information, particularly by means of posting URL, political actors might also apply a URL-based approach to selectively track contents behind hyperlinks shared in tweets, Facebook, and blog postings. This might provide additional meaningful insights, especially in case of tweets with a limited length of 140 characters.

Besides analysis purpose, other dimensions are different analysis approaches such as (1) topic/issue related, (2) opinion/sentiment related, (3) actor related, and (4) network related (Stieglitz and Dang-Xuan 2012).

Topic-Issue-Based Approach

For politicians and parties, it is important to identify and monitor political topics or issues, particularly those that might have a direct or indirect association with themselves as issues contain conflict potential and may evolve to a crisis (Wartick and Mahon 1994). In communication studies, such topic scanning and monitoring activities are referred to as issue management, which can serve the prevention of potential crisis or scandal that might lead to damages of own reputation. In order to identify topics, different methods of content analysis might be applied. Content analysis is a research technique for making replicable and valid inferences from texts (or other meaningful matter) to the contexts of their use. Given the massive amounts of social media data, quantitative automated (rather than qualitative manual) content analysis is preferred. Quantitative methods of content analysis are suitable to provide answers to a broad variety of questions, among them the classification of contents and the identification of recurring topics (Krippendorff 2004). Despite many advantages of automated approaches, manual content analysis is nevertheless needed to back up findings by automated analysis as it defines a set of practices that enable human coders to define reproducible categories for qualitative features of text more reliably. Many politicians and parties have also expressed their wish to have trending political topics predicted. Also, trends in social networks have recently been a major focus of interest among both research and industry (Agrawal et al. 2011). Recent advances in computer science and statistics have proposed a variety of algorithms to predict emerging topics. For example, many trend-detecting algorithms are based on the so-called hidden Markov models where observations of topics are trained by such models which in turn are saved in a library for topic’s prediction. Topics with similar life cycle are recorded and share a same model (e.g., Zeng et al. 2007; Liu and Guo 2011).

Opinion-Sentiment-Based Approach

Given the rapid growth of social media, people are enabled to express their views, opinions, or emotions on almost anything in forums, in blogs, and on SNS more than ever before. This applies particularly to communication in political networks, which is assumed to be of polarizing controversial nature. Opinions are important because whenever we need to make a decision, we want to hear others’ opinions. This is true not only for individuals but also for organizations. It becomes thus increasingly important for political institutions to get a feel of prevalent sentiment (positive or negative emotions) or opinions expressed by others about themselves as person or organization as well as on certain political topics. In recent years, sentiment analysis or opinion mining has emerged as distinct methods to study people’s opinions, appraisals, and emotions toward entities, events, and their attributes in a more thorough way (Liu 2010; Pang and Lee 2008). Until now, it is difficult for people to find relevant sites, extract related sentences with opinions, read them, summarize them, and organize them into usable forms. Automated opinion discovery and summarization systems are thus needed, which can be accomplished by sentiment analysis (Liu 2010). For example, sentiment analysis can be conducted on gathered data associated with identified topics from the topics/issue-related analysis above. However, analyses can also be performed the other way round. One can first collect emotionally charged postings and then try to identify top topics from that data set, i.e., extraction of emotionally charged topics. On top of sentiment analysis and opinion mining, automated content analysis might again be helpful in detecting users’ general perception of certain politicians or parties. In addition, qualitative manual content analysis might also be applied to get a more fine-grained picture (Krippendorff 2004).

Actor-Based Approach

Political institutions might be interested in identifying influential users who are ideologically or politically opposed to them. By monitoring those users, political actors might be able to have certain influence on those users’ opinion making by some forms of intervention such as directly seeking dialogues with them. To find such users, we suggest employing a combination of social network analysis and content analysis methods. Social network analysis studies the relations linking persons, organizations, interest groups, states, etc. by analyzing the structure of these relations (Scott and Carrington 2011). While social network analysis should help identify influential users, both automated and manual content analysis might shed light on the political preferences of them. Regarding measuring influence, there are a number of different measures of influence of an actor in a network. Basically, influence is determined by many factors, such as the novelty and resonance of their messages with those of their audience and the quality and frequency of the content they generate. In particular, resonance in terms retweets (Twitter) and comments (Facebook and blogs) can be modeled as edges connecting nodes that represent users within a social network. This way, different centrality metrics can be applied to measure influence (e.g., degree, betweenness, or eigenvector centrality) (Wasserman and Faust 1994; Scott and Carrington 2011). Besides revealing influential politically opposed actors, politicians and parties might also be interested in identifying actors who posted emotionally charged contents that are associated with them, particularly those with negative sentiment. Here, sentiment analysis might be helpful as discussed above.

Network-Based Approach

For political institutions, it might be sometimes even more helpful not only to identify not only certain influential users but also to detect influential politically relevant “communities,” particularly those whose members frequently mention or talk about them. Here, social network analysis might also be useful with different methods and algorithms for detecting communities. Community detection in social networks has attracted lots of attention in the domain of sociology. Some approaches (Newman and Girvan 2004) have performed better than the others for the discovery of communities in social networks. Researchers have also shown interest in discovering changing clusters in dynamic data and clustering the evolving data streams (Aggarwal et al. 2003). Some of the most prominent methods and algorithms for clustering groups or cliques are, for example, subtractive clustering method or rapid clustering method along with the algorithms “k-means” and “fuzzy c-means.”

Key Applications

Recently, conferences and journals addressed the topic of political networks and social media. Furthermore, new platforms have been developed that support political communication and political networks. Some examples can be found here: http://www.polinetworks.org/, http://www.volkalize.com/ or http://www.debatepolitics.com/.

Future Directions

As previous studies have shown, in the last few years, social media have become an important political communication channel. Nowadays political networks exist on all major social media platforms involving hundreds of thousands citizens. Consequently, social media communication is becoming increasingly important for politicians worldwide. A main driver for this development is social media’s abilities to enable voters and politicians to directly interact with each other. Therefore, political activities might gain more transparency, and citizens might be more involved into political decision-making processes. However, until now the potentials of political discussions in social media involving political institutions could not be exploited sufficiently. One reason for that is a lack of knowledge of politicians about current topics and discourses on different social media platforms. One can also observe the increasing relevance of and the need for analyzing political discussions on different social media platforms such as Twitter, Facebook, and weblogs.

Cross-References

References

  1. Aday S, Farrel H, Lynch M, Sides J, Kelly J, Zuckerman E (2010) Blogs and bullets: new media in contentious politics. Technical report, U.S. Institute of PeaceGoogle Scholar
  2. Aggarwal CC, Han J, Wang J, Yu PS (2003) A framework for clustering evolving data streams. In: Proceedings of VLDB’03, Berlin, pp 81–92Google Scholar
  3. Agrawal D, Budak C, El Abbadi A (2011) Information diffusion in social networks: observing and influencing societal interests. In: Proceedings of VLDB’11, Seattle, USAGoogle Scholar
  4. Bruns A, Stieglitz S (2014) Metrics for understanding communication on Twitter. In: Weller K, Bruns A, Burgess J, Mahrt M, Puschmann C (eds) Twitter and society, vol 89, 1st edn, Digital formations. Peter Lang, Publishing, New York/Bern/Berlin/Bruxelles/Frankfurt am Main/Oxford/Wien, pp 69–82Google Scholar
  5. Chadwick A (2006) Internet politics: states, citizens, and new communications technologies. Oxford University Press, New YorkGoogle Scholar
  6. Chen D, Tang J, Li J, Zhou L (2009) Discovering the staring people from social networks. In: WWW’09: proceedings of the 18th international conference on world wide web, New York. ACM, pp 1219–1220Google Scholar
  7. Dang-Xuan L, Stieglitz S (2012) Impact and diffusion of sentiment in political communication – an empirical analysis of political weblogs. Proceedings of the 6th International AAAI Conference on Weblogs and Social Media (ICWSM), pp 427–430Google Scholar
  8. Gruhl D, Nagarajan M, Pieper J, Robson C, Sheth A (2010) Multimodal social intelligence in a real-time dashboard system. Int J Very Large Data Bases 19(6):825–848CrossRefGoogle Scholar
  9. Kaplan AM, Haenlein M (2010) Users of the world, unite! The challenges and opportunities of Social Media. Bus Horiz 53(1):59–68CrossRefGoogle Scholar
  10. Karpf D (2009) Blogosphere research: a mixed-methods approach to rapidly changing systems. IEEE Intell Syst 24(5):67–70Google Scholar
  11. Kavanaugh A, Fox EA, Sheetz S, Yang S, Li LT, Whalen T, Shoemaker D, Natsev P, Xie L (2011) Social media use by government: from the routine to the critical. In: Proceedings of the 12th annual international digital government research conference: digital government innovation in challenging times, College Park, 12–15 June 2011Google Scholar
  12. Krippendorff K (2004) Content analysis: an introduction to its methodology. Sage, Thousand OaksGoogle Scholar
  13. Kushin M, Kitchener K (2009) Getting political on social network sites: exploring online political discourse on Facebook. First Monday 14(11)Google Scholar
  14. Larson K, Watson RT (2011) The value of social media: toward measuring social media strategies. In: Proceedings of international conference on information systems, ShanghaiGoogle Scholar
  15. Liu B (2010) Sentiment analysis: a multifaceted problem. IEEE Intell Syst 25:76–80CrossRefGoogle Scholar
  16. Liu R, Guo W (2011) HMM-based state prediction for Internet hot topic. In: Proceedings of the IEEE international conference on computer science and automation engineering (CSAE), ShanghaiGoogle Scholar
  17. McAfee A (2006) Enterprise 2.0: the Dawn of Emergent collaboration. MIT Sloan Manag Rev 47(3):20–28Google Scholar
  18. Meth S, Lee K, Yang, S (2015) Factors influencing Facebook users’ political participation: investigating the Cambodian Case. Proceedings of PACIS 2015, paper 44Google Scholar
  19. Newman ME, Girvan M (2004) Finding and evaluating community structure in networks. Phys Rev E Stat Nonlinear Soft Matter Phys 69(2 Pt 2):1–16Google Scholar
  20. Pang B, Lee L (2008) Opinion mining and sentiment analysis. Found Trends Inf Retr 2(1–2):1–135CrossRefGoogle Scholar
  21. Paris C, Wan S (2011) Listening to the community: social media monitoring tasks for improving government services. In: Proceedings of extended abstracts CHI. ACM, Vancouver, pp 2095–2100Google Scholar
  22. Scott J, Carrington PC (eds) (2011) Handbook of social network analysis. Sage, LondonGoogle Scholar
  23. Stieglitz S, Brockmann T (2013) The impact of smart-phones on E-participation. In: Proceedings of the 46th Hawaii international conference on system sciences (HICSS), Hawaii, pp 1734–1743Google Scholar
  24. Stieglitz S, Dang-Xuan L (2012) Social media and political communication – a social media analytics framework. Soc Netw Anal Min 3(4):1277–1291, SpringerCrossRefGoogle Scholar
  25. Stieglitz S, Dang-Xuan L (2013) Emotions and information diffusion in social media – sentiment of microblogs and sharing behavior. J Manag Info Syst 29(4):217–248CrossRefGoogle Scholar
  26. Stieglitz S, Dang-Xuan L, Brockmann T (2012) Usage of social media for political communication in Germany. In: Proceedings of PACIS 2012, Ho Chi Minh City, paper 341Google Scholar
  27. Stieglitz S, Dang-Xuan L, Bruns A, Neuberger C (2014) Social media analytics: an interdisciplinary approach and its implications for information systems. Bus Inf Syst Eng 6(2):89–96CrossRefGoogle Scholar
  28. Tumasjan A, Sprenger T, Sandner P, Welpe L (2011) Election forecasts with Twitter: how 140 characters reflect the political landscape. Soc Sci Comput Rev 29(4):402–418CrossRefGoogle Scholar
  29. Wartick S, Mahon J (1994) Toward a substantive definition of the corporate issue construct – a review and synthesis of the literature. Bus Soc 33:293–311CrossRefGoogle Scholar
  30. Wasserman S, Faust K (1994) Social network analysis: methods and applications. Cambridge University Press, New YorkCrossRefzbMATHGoogle Scholar
  31. Wattal S, Schuff D, Mandviwalla M, Williams C (2010) Web 2.0 and politics: the 2008 U.S. Presidential Election and an E-Politics research agenda. MIS Q 34(4):669–688Google Scholar
  32. Wigand RT, Wood JD, Mande DM (2010) Taming the social network jungle: from Web 2.0 to social media. In: Proceedings of the Americas conference on information systems, Lima, paper 416Google Scholar
  33. Zeng J, Zhang S, Wu C, Xie J (2007) Predictive model for internet public opinion. In: Proceedings of the fourth international conference on fuzzy systems and knowledge discovery (FSKD 2007), HaikouGoogle Scholar
  34. Zeng D, Chen H, Lusch R, Li S (2010) Social media analytics and intelligence. IEEE Intell Syst 25(6):13–16CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media LLC 2016

Authors and Affiliations

  1. 1.Department of Computer Science and Applied Cognitive Science, Professional Communication in Electronic Media/Social MediaUniversity of Duisburg-EssenDuisburgGermany

Section editors and affiliations

  • Talel Abdessalem
    • 1
  • Rokia Missaoui
    • 2
  1. 1.telecom-paristechParisFrance
  2. 2.Department of Computer Science and EngineeringUniversité du Québec en Outaouais (UQO)GatineauCanada