Related Research Articles
Personalized marketing, also known as one-to-one marketing or individual marketing, is a marketing strategy by which companies leverage data analysis and digital technology to deliver individualized messages and product offerings to current or prospective customers. Advancements in data collection methods, analytics, digital electronics, and digital economics, have enabled marketers to deploy more effective real-time and prolonged customer experience personalization tactics.
Personalization consists of tailoring a service or product to accommodate specific individuals. It is sometimes tied to groups or segments of individuals. Personalization involves collecting data on individuals, including web browsing history, web cookies, and location. Various organizations use personalization to improve customer satisfaction, digital sales conversion, marketing results, branding, and improved website metrics as well as for advertising. Personalization acts as a key element in social media and recommender systems. Personalization influences every sector of society— be it work, leisure, or citizenship.
In computing, a news aggregator, also termed a feed aggregator, content aggregator, feed reader, news reader, or simply an aggregator, is client software or a web application that aggregates digital content such as online newspapers, blogs, podcasts, and video blogs (vlogs) in one location for easy viewing. The updates distributed may include journal tables of contents, podcasts, videos, and news items.
Google Personalized Search is a personalized search feature of Google Search, introduced in 2004. All searches on Google Search are associated with a browser cookie record. When a user performs a search, the search results are not only based on the relevance of each web page to the search term, but also on which websites the user visited through previous search results. This provides a more personalized experience that can increase the relevance of the search results for the particular user. Such filtering may also have side effects, such as the creation of a filter bubble.
Social media optimization (SMO) is the use of a number of outlets and communities to generate publicity to increase the awareness of a product, service brand or event. Types of social media involved include RSS feeds, social news, bookmarking sites, and social networking sites such as Facebook, Instagram, Twitter, video sharing websites, and blogging sites. SMO is similar to search engine optimization (SEO) in that the goal is to generate web traffic and increase awareness for a website. SMO's focal point is on gaining organic links to social media content. In contrast, SEO's core is about reaching the top of the search engine hierarchy. In general, social media optimization refers to optimizing a website and its content to encourage more users to use and share links to the website across social media and networking sites.
In news media and social media, an echo chamber is an environment or ecosystem in which participants encounter beliefs that amplify or reinforce their preexisting beliefs by communication and repetition inside a closed system and insulated from rebuttal. An echo chamber circulates existing views without encountering opposing views, potentially resulting in confirmation bias. Echo chambers may increase social and political polarization and extremism. On social media, it is thought that echo chambers limit exposure to diverse perspectives, and favor and reinforce presupposed narratives and ideologies.
Social network advertising, also known as "social media targeting," is a group of terms used to describe forms of online advertising and digital marketing focusing on social networking services. One of the significant benefits of this type of advertising is that advertisers can take advantage of the users' demographic information, psychographics, and other data points to target their ads appropriately.
A content discovery platform is an implemented software recommendation platform which uses recommender system tools. It utilizes user metadata in order to discover and recommend appropriate content, whilst reducing ongoing maintenance and development costs. A content discovery platform delivers personalized content to websites, mobile devices and set-top boxes. A large range of content discovery platforms currently exist for various forms of content ranging from news articles and academic journal articles to television. As operators compete to be the gateway to home entertainment, personalized television is a key service differentiator. Academic content discovery has recently become another area of interest, with several companies being established to help academic researchers keep up to date with relevant academic content and serendipitously discover new content.
Personalized search is a web search tailored specifically to an individual's interests by incorporating information about the individual beyond the specific query provided. There are two general approaches to personalizing search results, involving modifying the user's query and re-ranking search results.
Ambient awareness (AmA) is a term used by social scientists to describe a form of peripheral social awareness through social media. This awareness is propagated from relatively constant contact with one's friends and colleagues via social networking platforms on the Internet. The term essentially defines the sort of omnipresent knowledge one experiences by being a regular user of these media outlets that allow a constant connection with one's social circle.
A filter bubble or ideological frame is a state of intellectual isolation that can result from personalized searches, recommendation systems, and algorithmic curation. The search results are based on information about the user, such as their location, past click-behavior, and search history. Consequently, users become separated from information that disagrees with their viewpoints, effectively isolating them in their own cultural or ideological bubbles, resulting in a limited and customized view of the world. The choices made by these algorithms are only sometimes transparent. Prime examples include Google Personalized Search results and Facebook's personalized news-stream.
Social media intelligence refers to 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.
News360 was a personalized news aggregation app for smartphones, tablets and the web. It attempted to learn a user's interests by analyzing their interaction with news stories on the app and using semantic analysis and natural language processing to create an Interest Graph and construct a unique feed of relevant content for each user. The app claims an audience of more than 4 million users.
Trapit is a comprehensive content curation service for business that offers content discovery, curation, and publishing to web, iPad, and social channels through its web application. The application pulls from text and video sources and offers built-in analytics and social scheduling tools. Trapit is a spin-off of SRI International that uses SRI's CALO technology to help users discover and publish content. It has acquired about $6.2 million in venture capital funding.
Sparrho combines human and artificial intelligence to help research professionals and layman users stay up-to-date with new scientific publications and patents. Sparrho's recommendation engine provides personalized scientific news-feeds by using proprietary machine learning algorithms to "aggregate, distill and recommend" relevant content. The platform aims to complement traditional methods of finding relevant academic material such as Google Scholar, and PubMed with a system which enables the serendipitous discovery of content and across relevant scientific fields.
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.
Facebook's Feed, formerly known as the News Feed, is a web feed feature for the social network. The feed is the primary system through which users are exposed to content posted on the network. Feed highlights information that includes profile changes, upcoming events, and birthdays, among other updates. Using a proprietary method, Facebook selects a handful of updates to show users every time they visit their feed, out of an average of 2,000 updates they can potentially receive. Over two billion people use Facebook every month, making the network's Feed the most viewed and most influential aspect of the news industry. The feature, introduced in 2006, was renamed "Feed" in 2022.
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.
Algorithmic radicalization is the concept that recommender algorithms on popular social media sites such as YouTube and Facebook drive users toward progressively more extreme content over time, leading to them developing radicalized extremist political views. Algorithms record user interactions, from likes/dislikes to amount of time spent on posts, to generate endless media aimed to keep users engaged. Through echo chamber channels, the consumer is driven to be more polarized through preferences in media and self-confirmation.
Algorithmic attention rents is a concept developed at University College London's Institute for Innovation and Public Purpose in a series of three papers sponsored by the Omidyar Network, and based on earlier work by Tim O’Reilly. It is used to explain how platforms, with a degree of market power, can degrade the quality of information shown to users in its algorithmic results and recommendations in order to increase their profits above the competitive level. Implications for AI systems is explored. This builds partly on their previous work on digital disclosures, published by the Oxford Review of Economic Policy.
References
- 1 2 "New exhibit highlights differences between algorithmic and human curation | University of Oxford". www.ox.ac.uk. December 8, 2022. Retrieved March 7, 2023.
- ↑ Berman, Ron; Katona, Zsolt (September 2016). "The Impact of Curation Algorithms on Social Network Content Quality and Structure". Working Papers.
- ↑ "Beating Social Media Algorithms: AI, Influence & Types - Jaskaran Saini". May 23, 2023.
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