Computational politics

Last updated

Computational politics is the intersection between computer science and political science. The area involves the usage of computational methods, such as analysis tools and prediction methods, to present the solutions to political sciences questions. [1] Researchers in this area use large sets of data to study user behavior. [2] Common examples of such works are building a classifier to predict users' political bias in social media or finding political bias in the news. This discipline is closely related with digital sociology. However, the main focus of computational politics is on political related problems and analysis.

Contents

Computational politics is often used in political campaigns to target individuals for advertising purposes. [3] [4] Recently, the new trends of Generative Artificial Intelligence have transformed the political campaigns around the world; such tools and technologies give more modalities to users and politicians for political participation, along with a rise in disinformation [5] .

Methods and applications

While there is no clearly defined data source for research done in computational politics, the most common sources are social networking websites and political debate transcripts. Various methods are used to computationally model the behavior of agents. [1] [6] Social network analysis is often used to model and analyze data from social networking sites, with nodes on a graph representing individual users and edges representing varying forms of interaction between users [1] . Natural language processing methods are used for text-based data, such as text from social media posts and political debate transcripts. For example, sentiment analysis, where algorithms are used to classify a piece of text as positive, negative, or neutral in sentiment, can be used to predict social media users' opinions on political parties or candidates. Various other machine learning algorithms are used to predict political bias in news sources, political affiliation of users of social networks, and whether political news articles are fake news or not. Computational models are often used to examine cognitive behavior associated with political contexts, including the connection between the brain and polarization or ideological thinking. [7]

Usage in political campaigns

The discipline of computational politics has emerged with growing use of social media and recent breakthroughs in computational methods. [1] [3] Social media has provided scientific researchers and campaign strategists with an unprecedented scale of latent, user-generated data, and there have been recent developments in computer science to store and manage large collections of data. [3] Computational politics represents a large shift in political science research, as lots of information can be efficiently collected on individuals rather than aggregates. [3] This information can be used to effectively target likely voters. [1] One of the first political campaigns to use computational politics was Barack Obama's 2012 campaign. An example of a data source used in the campaign was user data from a Facebook App created for it. The campaign developed a "likelihood of turnout" index to focus turning out voters who were already likely to prefer Obama. [3] [8] Since then, there has been a growing number of political data firms, which are private companies that sell voter registration tied to consumer data. [4] [8] One such data firm is i360, which is funded by the Koch brothers. Numerous clients of i360 saw victories in the 2014 United States Midterm Elections. [8]

Criticism

Campaign strategies relating to computational politics have been met with criticism. Some researchers raise concerns about voter privacy that come with new methods of targeting individual voters, as there is a lack of regulation surrounding protection of consumer data in the United States. [4] They highlight the information asymmetry that comes with the large amount of data that campaigns have concerning individual voters, while the voters don't know exactly what the campaigns are doing with their information. [3] [8] According to these researchers, the black box nature of the algorithms that handle voter data exacerbate this issue, as it is difficult to understand how data is being processed by the algorithms. [3] [8] Zeynep Tufekci, professor and sociologist, believes the information asymmetry caused by recent developments in computational politics will harm political discourse in the public sphere, particularly in interactions between political campaigns and voters, as ideas may be weighted less heavily for their own merit and more heavily based on who presents the idea, given the information political campaigns have on their potential voters. [3] These researchers also address the issues of discrimination that are rising with computational politics, as individuals who are predicted by models to be less likely to vote could be ignored in political marketing and outreach entirely. [3] [8] Tufecki states that political campaigns can present different advertisements based on which messages the potential voter is likely to be sympathetic to, allowing politicians to base their platform off of small issues that will efficiently mobilize niche groups of voters, while less focus may be spent on larger, more broadly important issues. [3] Kwame Akosah, a voting rights advocate, writes that algorithms can be used to discriminate against protected classes. [8] Additionally, he states that the knowledge of individuals being tracked by data brokers can create a chilling effect in political discourse. [8] Polarisation and privacy have been well-discussed issues in this domain, however, the massive adaptability of social media platforms has attracted the organised use of social media for opinion engineering during crisis, as seen in Russia-Ukraine 2022 or natural crisis like COVID-19 pandemic. [9] Such use of social media is associated with Information warfare and has raised questions on the ethics and regulation of data ownership, fair algorithms, and the use of jury based methods for the content moderation. [9]

Related Research Articles

<span class="mw-page-title-main">Computational biology</span> Branch of biology

Computational biology refers to the use of techniques in computer science, data analysis, mathematical modeling and computational simulations to understand biological systems and relationships. An intersection of computer science, biology, and data science, the field also has foundations in applied mathematics, molecular biology, cell biology, chemistry, and genetics.

Machine learning (ML) is a field of study in artificial intelligence concerned with the development and study of statistical algorithms that can learn from data and generalize to unseen data, and thus perform tasks without explicit instructions. Advances in the field of deep learning have allowed neural networks to surpass many previous approaches in performance.

<span class="mw-page-title-main">Social network analysis</span> Analysis of social structures using network and graph theory

Social network analysis (SNA) is the process of investigating social structures through the use of networks and graph theory. It characterizes networked structures in terms of nodes and the ties, edges, or links that connect them. Examples of social structures commonly visualized through social network analysis include social media networks, meme proliferation, information circulation, friendship and acquaintance networks, business networks, knowledge networks, difficult working relationships, collaboration graphs, kinship, disease transmission, and sexual relationships. These networks are often visualized through sociograms in which nodes are represented as points and ties are represented as lines. These visualizations provide a means of qualitatively assessing networks by varying the visual representation of their nodes and edges to reflect attributes of interest.

<span class="mw-page-title-main">Political campaign</span> Attempt to influence the decision making process within a specific group

A political campaign is an organized effort which seeks to influence the decision making progress within a specific group. In democracies, political campaigns often refer to electoral campaigns, by which representatives are chosen or referendums are decided. In modern politics, the most high-profile political campaigns are focused on general elections and candidates for head of state or head of government, often a president or prime minister.

A recommender system (RecSys), or a recommendation system (sometimes replacing system with terms such as platform, engine, or algorithm), is a subclass of information filtering system that provides suggestions for items that are most pertinent to a particular user. Recommender systems are particularly useful when an individual needs to choose an item from a potentially overwhelming number of items that a service may offer.

Sentiment analysis is the use of natural language processing, text analysis, computational linguistics, and biometrics to systematically identify, extract, quantify, and study affective states and subjective information. Sentiment analysis is widely applied to voice of the customer materials such as reviews and survey responses, online and social media, and healthcare materials for applications that range from marketing to customer service to clinical medicine. With the rise of deep language models, such as RoBERTa, also more difficult data domains can be analyzed, e.g., news texts where authors typically express their opinion/sentiment less explicitly.

Political methodology is a subfield of political science that studies the quantitative and qualitative methods used to study politics and draw conclusions using data. Quantitative methods combine statistics, mathematics, and formal theory. Political methodology is often used for positive research, in contrast to normative research. Psephology, a skill or technique within political methodology, is the "quantitative analysis of elections and balloting".

Privacy-enhancing technologies (PET) are technologies that embody fundamental data protection principles by minimizing personal data use, maximizing data security, and empowering individuals. PETs allow online users to protect the privacy of their personally identifiable information (PII), which is often provided to and handled by services or applications. PETs use techniques to minimize an information system's possession of personal data without losing functionality. Generally speaking, PETs can be categorized as either hard or soft privacy technologies.

The social data revolution is the shift in human communication patterns towards increased personal information sharing and its related implications, made possible by the rise of social networks in the early 2000s. This phenomenon has resulted in the accumulation of unprecedented amounts of public data.

Learning analytics is the measurement, collection, analysis and reporting of data about learners and their contexts, for purposes of understanding and optimizing learning and the environments in which it occurs. The growth of online learning since the 1990s, particularly in higher education, has contributed to the advancement of Learning Analytics as student data can be captured and made available for analysis. When learners use an LMS, social media, or similar online tools, their clicks, navigation patterns, time on task, social networks, information flow, and concept development through discussions can be tracked. The rapid development of massive open online courses (MOOCs) offers additional data for researchers to evaluate teaching and learning in online environments.

Social media intelligence comprises the collective tools and solutions that allow organizations to analyze conversations, respond to synchronize social signals, and synthesize social data points into meaningful trends and analysis, based on the user's needs. Social media intelligence allows one to utilize intelligence gathering from social media sites, using both intrusive or non-intrusive means, from open and closed social networks. This type of intelligence gathering is one element of OSINT.

Computational social science is an interdisciplinary academic sub-field concerned with computational approaches to the social sciences. This means that computers are used to model, simulate, and analyze social phenomena. It has been applied in areas such as computational economics, computational sociology, computational media analysis, cliodynamics, culturomics, nonprofit studies. It focuses on investigating social and behavioral relationships and interactions using data science approaches, network analysis, social simulation and studies using interactive systems.

Social media mining is the process of obtaining data from user-generated content on social media in order to extract actionable patterns, form conclusions about users, and act upon the information. Mining supports targeting advertising to users or academic research. The term is an analogy to the process of mining for minerals. Mining companies sift through raw ore to find the valuable minerals; likewise, social media mining sifts through social media data in order to discern patterns and trends about matters such as social media usage, online behaviour, content sharing, connections between individuals, buying behaviour. These patterns and trends are of interest to companies, governments and not-for-profit organizations, as such organizations can use the analyses for tasks such as design strategies, introduce programs, products, processes or services.

<span class="mw-page-title-main">Population informatics</span>

The field of population informatics is the systematic study of populations via secondary analysis of massive data collections about people. Scientists in the field refer to this massive data collection as the social genome, denoting the collective digital footprint of our society. Population informatics applies data science to social genome data to answer fundamental questions about human society and population health much like bioinformatics applies data science to human genome data to answer questions about individual health. It is an emerging research area at the intersection of SBEH sciences, computer science, and statistics in which quantitative methods and computational tools are used to answer fundamental questions about our society. [[File:Data science.png|alt=Data Science|thumb|Data Science]

A social bot, also described as a social AI or social algorithm, is a software agent that communicates autonomously on social media. The messages it distributes can be simple and operate in groups and various configurations with partial human control (hybrid) via algorithm. Social bots can also use artificial intelligence and machine learning to express messages in more natural human dialogue.

<span class="mw-page-title-main">Algorithmic bias</span> Technological phenomenon with social implications

Algorithmic bias describes systematic and repeatable errors in a computer system that create "unfair" outcomes, such as "privileging" one category over another in ways different from the intended function of the algorithm.

Social media use in politics refers to the use of online social media platforms in political processes and activities. Political processes and activities include all activities that pertain to the governance of a country or area. This includes political organization, global politics, political corruption, political parties, and political values. The media's primary duty is to present us with information and alert us when events occur. This information may affect what we think and the actions we take. The media can also place pressure on the government to act by signaling a need for intervention or showing that citizens want change

Soft computing is an umbrella term used to describe types of algorithms that produce approximate solutions to unsolvable high-level problems in computer science. Typically, traditional hard-computing algorithms heavily rely on concrete data and mathematical models to produce solutions to problems. Soft computing was coined in the late 20th century. During this period, revolutionary research in three fields greatly impacted soft computing. Fuzzy logic is a computational paradigm that entertains the uncertainties in data by using levels of truth rather than rigid 0s and 1s in binary. Next, neural networks which are computational models influenced by human brain functions. Finally, evolutionary computation is a term to describe groups of algorithm that mimic natural processes such as evolution and natural selection.

<span class="mw-page-title-main">Michal Kosinski</span> American-Polish computational psychologist

Michal Kosinski is an associate professor of Organizational Behavior at Stanford University, a computational psychologist, and a psychometrician. He studies the psychological processes in Large Language Models (LLMs), as well as AI and big data to model and predict human behavior.

Social data science is an interdisciplinary field that addresses social science problems by applying or designing computational and digital methods. As the name implies, Social Data Science is located primarily within the social science, but it relies on technical advances in fields like data science, network science, and computer science. The data in Social Data Science is always about human beings and derives from social phenomena, and it could be structured data or unstructured data. The goal of Social Data Science is to yield new knowledge about social networks, human behavior, cultural ideas and political ideologies. A social data scientist combines domain knowledge and specialized theories from the social sciences with programming, statistical and other data analysis skills.

References

  1. 1 2 3 4 5 Haq, Ehsan Ul; Braud, Tristan; Kwon, Young D.; Hui, Pan (2020). "A Survey on Computational Politics". IEEE Access. 8: 197379–197406. arXiv: 1908.06069 . doi:10.1109/ACCESS.2020.3034983. ISSN   2169-3536.
  2. ↑ Winston, Patrick H.; Finlayson, Mark A. "Computational Politics". ResearchGate. S2CID   7589841.
  3. 1 2 3 4 5 6 7 8 9 10 Tufekci, Zeynep (2014-07-02). "Engineering the public: Big data, surveillance and computational politics". First Monday. doi: 10.5210/fm.v19i7.4901 . ISSN   1396-0466.
  4. 1 2 3 Chester, Jeff; Montgomery, Kathryn C. (2017-12-31). "The role of digital marketing in political campaigns". Internet Policy Review. 6 (4). doi:10.14763/2017.4.773. hdl: 10419/214047 . ISSN   2197-6775.
  5. ↑ Haq, Ehsan-Ul; Zhu, Yiming; Hui, Pan; Tyson, Gareth (2024-05-13). "History in Making: Political Campaigns in the Era of Artificial Intelligence-Generated Content". Companion Proceedings of the ACM Web Conference 2024. WWW '24. New York, NY, USA: Association for Computing Machinery: 1115–1118. doi:10.1145/3589335.3652000. ISBN   979-8-4007-0172-6.
  6. ↑ Kim, Sung-youn (2020-09-28). "Computational Models of Political Decision Making". Oxford Research Encyclopedia of Politics. doi:10.1093/acrefore/9780190228637.013.881. ISBN   978-0-19-022863-7 . Retrieved 2021-06-04.
  7. ↑ Zmigrod, Leor; Tsakiris, Manos (2021-04-12). "Computational and neurocognitive approaches to the political brain: key insights and future avenues for political neuroscience". Philosophical Transactions of the Royal Society B: Biological Sciences. 376 (1822): 20200130. doi:10.1098/rstb.2020.0130. PMC   7935136 . PMID   33611999.
  8. 1 2 3 4 5 6 7 8 Akosah, Kwame N. (2014–2015). "Cracking the One-Way Mirror: How Computational Politics Harms Voter Privacy, and Proposed Regulatory Solutions". Fordham Intellectual Property, Media & Entertainment Law Journal. 25: 1007.
  9. 1 2 Haq, Ehsan-Ul; Tyson, Gareth; Braud, Tristan; Hui, Pan (2022-06-28). "Weaponising Social Media for Information Divide and Warfare". Proceedings of the 33rd ACM Conference on Hypertext and Social Media. HT '22. New York, NY, USA: Association for Computing Machinery. pp. 259–262. doi:10.1145/3511095.3536372. ISBN   978-1-4503-9233-4. S2CID   249872702.
This page is based on this Wikipedia article
Text is available under the CC BY-SA 4.0 license; additional terms may apply.
Images, videos and audio are available under their respective licenses.