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. Researchers in this area use large sets of data to study user behavior. [1] 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. [2] [3]

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. [4] Various methods are used to computationally model the behavior of agents. [5] [4] 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. [4] Natural language processing methods are used for text-based data, such as text from social media posts and political debate transcripts. [4] 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. [4] 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. [4] Computational models are often used to examine cognitive behavior associated with political contexts, including the connection between the brain and polarization or ideological thinking. [6]

Usage in political campaigns

The discipline of computational politics has emerged with growing use of social media and recent breakthroughs in computational methods. [4] [2] 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. [2] Computational politics represents a large shift in political science research, as lots of information can be efficiently collected on individuals rather than aggregates. [4] [2] This information can be used to effectively target likely voters. 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. [2] [7] Since then, there has been a growing number of political data firms, which are private companies that sell voter registration tied to consumer data. [3] [7] 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. [7]

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. [3] 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. [2] [7] 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. [2] [7] 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. [2] 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. [2] [7] 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. [2] Kwame Akosah, a voting rights advocate, writes that algorithms can be used to discriminate against protected classes. [7] Additionally, he states that the knowledge of individuals being tracked by data brokers can create a chilling effect in political discourse. [7] Interdisciplinary researcher Ashu M.G. Solo points out that a positive aspect of computational politics is that it will lead to a more efficient use of spending in political campaigns. [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">Data mining</span> Process of extracting and discovering patterns in large data sets

Data mining is the process of extracting and discovering patterns in large data sets involving methods at the intersection of machine learning, statistics, and database systems. Data mining is an interdisciplinary subfield of computer science and statistics with an overall goal of extracting information from a data set and transforming the information into a comprehensible structure for further use. Data mining is the analysis step of the "knowledge discovery in databases" process, or KDD. Aside from the raw analysis step, it also involves database and data management aspects, data pre-processing, model and inference considerations, interestingness metrics, complexity considerations, post-processing of discovered structures, visualization, and online updating.

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

Computational biology refers to the use of data analysis, mathematical modeling and computational simulations to understand biological systems and relationships. An intersection of computer science, biology, and big data, the field also has foundations in applied mathematics, chemistry, and genetics. It differs from biological computing, a subfield of computer engineering which uses bioengineering to build computers.

<span class="mw-page-title-main">Machine learning</span> Study of algorithms that improve automatically through experience

Machine learning (ML) is an umbrella term for solving problems for which development of algorithms by human programmers would be cost-prohibitive, and instead the problems are solved by helping machines "discover" their "own" algorithms, without needing to be explicitly told what to do by any human-developed algorithms. Recently, generative artificial neural networks have been able to surpass results of many previous approaches. Machine-learning approaches have been applied to large language models, computer vision, speech recognition, email filtering, agriculture and medicine, where it is too costly to develop algorithms to perform the needed tasks.

A recommender system, or a recommendation system, is a subclass of information filtering system that provide suggestions for items that are most pertinent to a particular user. Typically, the suggestions refer to various decision-making processes, such as what product to purchase, what music to listen to, or what online news to read. 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.

<span class="mw-page-title-main">Computational sociology</span> Branch of the discipline of sociology

Computational sociology is a branch of sociology that uses computationally intensive methods to analyze and model social phenomena. Using computer simulations, artificial intelligence, complex statistical methods, and analytic approaches like social network analysis, computational sociology develops and tests theories of complex social processes through bottom-up modeling of social interactions.

Computational science, also known as scientific computing, technical computing or scientific computation (SC), is a division of science that uses advanced computing capabilities to understand and solve complex physical problems. This includes

Social search is a behavior of retrieving and searching on a social searching engine that mainly searches user-generated content such as news, videos and images related search queries on social media like Facebook, LinkedIn, Twitter, Instagram and Flickr. It is an enhanced version of web search that combines traditional algorithms. The idea behind social search is that instead of ranking search results purely based on semantic relevance between a query and the results, a social search system also takes into account social relationships between the results and the searcher. The social relationships could be in various forms. For example, in LinkedIn people search engine, the social relationships include social connections between searcher and each result, whether or not they are in the same industries, work for the same companies, belong the same social groups, and go the same schools, etc.

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 hard and 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.

<span class="mw-page-title-main">Data science</span> Interdisciplinary field of study on deriving knowledge and insights from data

Data science is an interdisciplinary academic field that uses statistics, scientific computing, scientific methods, processes, algorithms and systems to extract or extrapolate knowledge and insights from noisy, structured, and unstructured data.

<span class="mw-page-title-main">User profile</span> Data about an individual user

A user profile is a collection of settings and information associated with a user. It contains critical information that is used to identify an individual, such as their name, age, portrait photograph and individual characteristics such as knowledge or expertise. User profiles are most commonly present on social media websites such as Facebook, Instagram, and LinkedIn; and serve as voluntary digital identity of an individual, highlighting their key features and traits. In personal computing and operating systems, user profiles serve to categorise files, settings, and documents by individual user environments, known as ‘accounts’, allowing the operating system to be more friendly and catered to the user. Physical user profiles serve as identity documents such as passports, driving licenses and legal documents that are used to identify an individual under the legal system.

<span class="mw-page-title-main">Google Flu Trends</span> Former web service operated by Google

Google Flu Trends (GFT) was a web service operated by Google. It provided estimates of influenza activity for more than 25 countries. By aggregating Google Search queries, it attempted to make accurate predictions about flu activity. This project was first launched in 2008 by Google.org to help predict outbreaks of flu.

<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, or 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.

Local differential privacy (LDP) is a model of differential privacy with the added requirement that even if an adversary has access to the personal responses of an individual in the database, that adversary will still be unable to learn too much about the user's personal data. This is contrasted with global differential privacy, a model of differential privacy that incorporates a central aggregator with access to the raw data.

Automated decision-making (ADM) involves the use of data, machines and algorithms to make decisions in a range of contexts, including public administration, business, health, education, law, employment, transport, media and entertainment, with varying degrees of human oversight or intervention. ADM involves large-scale data from a range of sources, such as databases, text, social media, sensors, images or speech, that is processed using various technologies including computer software, algorithms, machine learning, natural language processing, artificial intelligence, augmented intelligence and robotics. The increasing use of automated decision-making systems (ADMS) across a range of contexts presents many benefits and challenges to human society requiring consideration of the technical, legal, ethical, societal, educational, economic and health consequences.

<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 humans in a digital environment using computational methods, AI and Big Data.

References

  1. ↑ Winston, Patrick H.; Finlayson, Mark A. "Computational Politics". ResearchGate. S2CID   7589841.
  2. 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.
  3. 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.
  4. 1 2 3 4 5 6 7 8 E. U. Haq, T. Braud, Y. D. Kwon and P. Hui, "A Survey on Computational Politics," in IEEE Access, vol. 8, pp. 197379-197406, 2020, doi: 10.1109/ACCESS.2020.3034983.
  5. ↑ 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.
  6. ↑ 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.
  7. 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.
  8. ↑ Solo, Ashu MG. "The new fields of public policy engineering, political engineering, computational public policy, and computational politics." Proceedings of the International Conference on e-Learning, e-Business, Enterprise Information Systems, and e-Government (EEE). The Steering Committee of The World Congress in Computer Science, Computer Engineering and Applied Computing (WorldComp), 2011.
  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.