Private cloud computing infrastructure is a category of cloud computing that provides comparable benefits to public cloud systems, such as self-service and scalability, but it does so via a proprietary framework. In contrast to public clouds, which cater to multiple entities, a private cloud is specifically designed for the requirements and objectives of one organization. == Definition == A private cloud computing infrastructure constitutes a distinctive model of cloud computing that facilitates a secure and distinct cloud environment where only the intended client can function. It can either be physically housed in the organization's in-house data center or be managed by a third-party provider. In a private cloud, the infrastructure and services are always sustained on a private network, and both the hardware and software are devoted exclusively to a single organization. == History == The concept of private cloud infrastructure started to take shape around the mid-2000s, coinciding with the rise of other cloud computing forms. It came into existence as a solution to the shortcomings of public clouds, particularly concerns over data control, security, and network performance. IT departments began to mirror the automation and self-service features of the public cloud in their data centers. Over time, these services became more advanced, and private cloud technology has been refined to address businesses and organizations' diverse needs. == Architecture == Private cloud computing infrastructure generally involves a mix of hardware, network infrastructure, and virtualization software. The hardware, often referred to as a cloud server or cloud array, consists of a server rack or a collection of server racks containing the storage and processors that constitute the cloud. The virtualization software, such as Hyper-V, OpenStack, or VMWare, establishes and oversees virtual machines with which users interact. The network infrastructure connects the private cloud to users and may facilitate connectivity with other on-premises data centers or clouds. == Applications == Private cloud infrastructures are usually utilized by medium to large businesses and organizations that need robust control over their data, have extensive computing needs, or have specific regulatory or compliance obligations. This includes healthcare organizations, government agencies, financial institutions, and any business that needs to process and store large data volumes.
Supper (Spotify)
Supper is a web-based application on the Spotify digital music streaming platform. The Supper app was born from a group of friends who had backgrounds in the music and gastronomy industries. Digital music solutions company Artisan Council later executed it. The app now sits in the top 40 applications on Spotify. == About == The Supper Spotify application matches recipes for all occasions and skill levels with a playlist for both preparation and presentation, as envisioned by the chefs themselves. Supper is credited with being one of the first apps to pair music with food. Playing on the social nature of music and food culture, users can seamlessly experience both for the first time with real time music streaming. == Supper.mx == In May 2014 Supper was launched outside of the Spotify streaming platform. Though still in partnership with Spotify, supper.mx allows users to view Supper's music + food collaborations on mobile, tablet and desktop, without the need to download Spotify directly. == Curators == All of the recipes and playlists featured on the Supper app come straight from a growing network of tastemakers, including chefs, musicians and institutions around the world. Each month the recipes and playlists are updated in conjunction with current holidays, events and seasons. === Launch === Launching in October 2013 the first edition of Supper featured content from a range of eating institutions and culture makers from the US and Australia. Brooklyn Bowl (Brooklyn) Roberta's Pizza (Brooklyn) Fancy Hanks (Melbourne) The Foresters/Queenies Upstairs (Sydney) Hipstamatic Panama House (Bondi) Sweetwater Inn (Melbourne) Soul Clap (Syd record label) Yellow Birds (Melbourne) === November 2013 === Yardbird (Hong Kong) Sonoma Bakery (Sydney) Do or Dine (Brooklyn) Cameo Gallery (Brooklyn) Hypertrak (Blog) Blue Smoke (NYC) The Crepes of Wrath (Blog) Willin Low // Wild Rocket - Wild Oats - Relish === December 2013 === The Copper Mill (Sydney) Thug Kitchen Mamak (Sydney) Tutu's (Brooklyn) Chin Chin (Melbourne) Flat Iron Steak (London) Greasy Spoon (Copenhagen) === January 2014 === Mexicali Taco & Co. (LA) Church & State (LA) Salts Cure (LA) Nopa (SF) L & E Oyster (LA) 4100 bar (LA) Golden Gopher (LA) The Pie Hole (LA) State Bird Provisions (SF) === Momofuku === In February 2014 Supper teamed up with restaurant heavy weights Momofuku. The recipes featured came from their iconic New York, Toronto and Sydney restaurants. Head office also got involved with an instructional from Brand Director Sue Chan on how to paint Momofuku vibes on to any party. === SXSW === March sees the Supper team migrate to Austin, Texas for SXSW, bringing together the best eateries the city has to offer as well as the music that has influenced them. Restaurants and eateries on board in 2014 included: The Backspace Kelis Swifts Attic Uchi Jackalope Paul Qui/East Side King Thai Kun Wonderland Hole in the Wall Justine's Brasserie The Liberty === Kelis === In April 2014 Kelis presented 5 of her recipes paired with a personal playlist for Supper. Kelis shared her recipes for apple farro, jerk ribs, New York vanilla bean cheesecake and Jerk Ribs. The Kelis/Supper collaboration coincided with the release of Kelis' 2014 album titled 'Food'. === Roberta's Pizza === In May 2014 Bushwick's Roberta's Pizza was guest curator on the Supper app and website. Included in their selections were restaurants and bars from across New York including Bun-ker Vietnamese, Old Stanley's Bar, St. Anselm, Chuko, Frank's Cocktail Lounge, Junior's Cheesecake, Xi'an Famous Foods, Xe Lua, 124 Old Rabbit and Yuji Ramen.
TikTokification
TikTokification (also written TikTok-ification) is a term used to describe the widespread adoption of TikTok's short-form, vertical video format and its algorithmic content-delivery model across the broader social media landscape. The phenomenon encompasses the strategic and cultural changes made by competing platforms such as Instagram, YouTube, Facebook, Snapchat, and LinkedIn in response to TikTok's global dominance. Beyond platform design, the term is also used more broadly to describe shifts in media consumption habits, advertising strategies, and, more critically, the potential cognitive and psychological effects associated with constant short-form video consumption. == Background == === Origins of short-form video === The short-form video format predates TikTok. Vine, launched in 2013, popularised six-second looping videos before shutting down in 2017. TikTok itself, known as Douyin in the Chinese market, was created by the Chinese technology company ByteDance in September 2016. Following its international expansion and its 2018 merger with Musical.ly, TikTok grew rapidly. By 2020, the application had surpassed two billion total downloads worldwide, with over 800 million monthly active users. A key driver of TikTok's success was its recommendation algorithm. The platform's "For You Page" (FYP) serves content to users based on behaviour rather than follower count, making it possible for unknown creators to achieve widespread reach organically. Analysts noted that TikTok serves "fast, visually engaging, and authentic videos that feel more like entertainment than advertising," fundamentally reshaping consumer expectations of digital content. TikTok has been described as "the center of the internet for young people," where users go for entertainment, news, trends, and shopping. As of the mid-2020s, TikTok had approximately 1.12 billion monthly active users. == Platform responses == TikTok's success compelled nearly every major social media platform to restructure its product around short-form video. In 2020, Instagram launched Reels and YouTube launched Shorts, both directly in response to TikTok's growth. Platforms like Meta's Instagram Reels and Google's YouTube Shorts subsequently expanded aggressively, launching new features, creator tools, and even considering separate standalone applications to compete. LinkedIn, traditionally a professional networking site, began experimenting with TikTok-style short-form vertical video feeds. Facebook launched a singular unified video feed combining Reels, long videos, and live videos, similar in structure to TikTok's feed. Snapchat redesigned its application to combine Stories and Spotlight into a unified entertainment feed. YouTube extended its Shorts format to allow videos up to three minutes in length, up from the previous limit of sixty seconds, as of October 2024. Despite these adaptations, experts noted that none of TikTok's rivals had matched its algorithmic precision as of mid-2025. == Societal and cultural impact == === Media and journalism === News organisations have also been affected by TikTokification. Short-form video grew rapidly as a format for news content, driven in large part by TikTok's popularity. According to Pew Research Center, 17% of adults in the United States reported regularly getting news from TikTok in 2024, with 63% of teenagers saying they used the platform as a news source. In response, major publishers began creating bespoke short-form content for TikTok's audience, with organisations such as the BBC building dedicated internal TikTok teams. === Advertising and commerce === TikTokification has had significant effects on the advertising industry. US social video advertising spending was projected to surpass linear television advertising spending for the first time in 2025. Global social commerce sales were projected to reach approximately $900 billion in 2025, with platforms like Douyin and TikTok driving a large share of that growth. TikTok itself generated an estimated $23.6 billion in advertising revenue in 2024. Short-form video has been described as bridging the gap between brand awareness and direct conversion. Surveys have found that consumers trust user-generated content 8.7 times more than influencer content and 6.6 times more than branded content, prompting brands to favour creator-led video formats. === Attention spans and cognitive effects === A growing body of research has examined the cognitive consequences of heavy short-form video consumption, a set of effects sometimes referred to as "TikTok Brain." A large systematic review and meta-analysis published in Psychological Bulletin, analysing data from 98,299 participants across 71 studies, found that the more short-form video content a person watches, the poorer their cognitive performance in attention and inhibitory control. The review also found that greater engagement with short-form video was associated with higher levels of anxiety, depression, and stress, as well as sleep disturbances. The platform's inherent demand for engaging content has resulted in the proliferation of sludge content, a genre of split screen video with the main video on the top and an unrelated attention-grabbing video on the bottom, typically repetitive gameplay (notably of the endless runner mobile game Subway Surfers) or oddly satisfying videos, designed to maximize viewer retention in cases where the main video may appear uninteresting and would normally cause the viewer to skip it. Sludge content is often described as overstimulating, reflecting and contributing to declining attention spans, though the scholarly evidence supporting such claims is not conclusive. Dr. Yann Poncin, associate professor at the Child Study Center at Yale University, noted that "infinite scrolling and short-form video are designed to capture your attention in short bursts," contrasting this with earlier entertainment formats that guided audiences through longer narratives. Research suggests that children and teenagers may be particularly vulnerable, with early exposure to rapid frame changes potentially conditioning the brain's neural pathways to require constant stimulation, making it more challenging to engage with slower-paced activities. A separate study published in Nature Communications by researchers at the Technical University of Denmark documented a notable decrease in collective attention span over time, attributing it in part to the increasing volume and pace of content production and consumption online. Researchers caution, however, that the majority of relevant studies are cross-sectional, meaning they capture data at a single point in time and cannot establish causality. It remains possible that individuals with pre-existing conditions such as anxiety or attention deficits may be more likely to engage heavily with these platforms as a coping mechanism. === Academic and sociological analysis === Scholars have framed TikTokification within the context of the attention economy. A 2024 academic analysis described TikTok as representing "a new paradigm of social media communication" shaped by youth culture, mobile technology, and the economics of attention, in which spectators become active contributors to a shared content pipeline. The same analysis noted that TikTok "reflects a new mode of communication influenced by avant-garde cinema, the use of mobile technology, and the social habits of particular social groups." US social media users were projected to spend 61.1% of their time on social networks watching videos in 2025, up from 33.3% in 2019, before TikTok became widely popular, underscoring the scale of the behavioural shift. == Monetisation challenges == Despite high engagement levels, monetising short-form video has remained difficult for platforms and creators alike. Unlike long-form YouTube content, short clips offer limited space for advertisers to insert advertisements. YouTube Shorts pays approximately four cents per 1,000 views, considerably less than its long-form counterpart. From 2025 onward, platforms began introducing creator funds, advertisements, and AI-driven content recommendations as part of broader efforts to make short-form video economically sustainable for creators.
Key-agreement protocol
In cryptography, a key-agreement protocol is a protocol whereby two (or more) parties generate a cryptographic key as a function of information provided by each honest party so that no party can predetermine the resulting value. In particular, all honest participants influence the outcome. A key-agreement protocol is a specialisation of a key-exchange protocol. At the completion of the protocol, all parties share the same key. A key-agreement protocol precludes undesired third parties from forcing a key choice on the agreeing parties. A secure key agreement can ensure confidentiality and data integrity in communications systems, ranging from simple messaging applications to complex banking transactions. Secure agreement is defined relative to a security model, for example the Universal Model. More generally, when evaluating protocols, it is important to state security goals and the security model. For example, it may be required for the session key to be authenticated. A protocol can be evaluated for success only in the context of its goals and attack model. An example of an adversarial model is the Dolev–Yao model. In many key exchange systems, one party generates the key, and sends that key to the other party; the other party has no influence on the key. == Exponential key exchange == The first publicly known public-key agreement protocol that meets the above criteria was the Diffie–Hellman key exchange, in which two parties jointly exponentiate a generator with random numbers, in such a way that an eavesdropper cannot feasibly determine what the resultant shared key is. Exponential key agreement in and of itself does not specify any prior agreement or subsequent authentication between the participants. It has thus been described as an anonymous key agreement protocol. == Symmetric key agreement == Symmetric key agreement (SKA) is a method of key agreement that uses solely symmetric cryptography and cryptographic hash functions as cryptographic primitives. It is related to symmetric authenticated key exchange. SKA may assume the use of initial shared secrets or a trusted third party with whom the agreeing parties share a secret is assumed. If no third party is present, then achieving SKA can be trivial: we tautologically assume that two parties that share an initial secret and have achieved SKA. SKA contrasts with key-agreement protocols that include techniques from asymmetric cryptography, such as key encapsulation mechanisms. The initial exchange of a shared key must be done in a manner that is private and integrity-assured. Historically, this was achieved by physical means, such as by using a trusted courier. An example of a SKA protocol is the Needham–Schroeder protocol. It establishes a session key between two parties on the same network, using a server as a trusted third party. The original Needham–Schroeder protocol is vulnerable to a replay attack. Timestamps and nonces are included to fix this attack. It forms the basis for the Kerberos protocol. === Types of key agreement === Boyd et al. classify two-party key agreement protocols according to two criteria as follows: whether a pre-shared key already exists or not the method of generating the session key. The pre-shared key may be shared between the two parties, or each party may share a key with a trusted third party. If there is no secure channel (as may be established via a pre-shared key), it is impossible to create an authenticated session key. The session key may be generated via: key transport, key agreement and hybrid. If there is no trusted third party, then the cases of key transport and hybrid session key generation are indistinguishable. SKA is concerned with protocols in which the session key is established using only symmetric primitives. == Authentication == Anonymous key exchange, like Diffie–Hellman, does not provide authentication of the parties, and is thus vulnerable to man-in-the-middle attacks. A wide variety of cryptographic authentication schemes and protocols have been developed to provide authenticated key agreement to prevent man-in-the-middle and related attacks. These methods generally mathematically bind the agreed key to other agreed-upon data, such as the following: public–private key pairs shared secret keys passwords === Public keys === A widely used mechanism for defeating such attacks is the use of digitally signed keys that must be integrity-assured: if Bob's key is signed by a trusted third party vouching for his identity, Alice can have considerable confidence that a signed key she receives is not an attempt to intercept by Eve. When Alice and Bob have a public-key infrastructure, they may digitally sign an agreed Diffie–Hellman key, or exchanged Diffie–Hellman public keys. Such signed keys, sometimes signed by a certificate authority, are one of the primary mechanisms used for secure web traffic (including HTTPS, SSL or TLS protocols). Other specific examples are MQV, YAK and the ISAKMP component of the IPsec protocol suite for securing Internet Protocol communications. However, these systems require care in endorsing the match between identity information and public keys by certificate authorities in order to work properly. === Hybrid systems === Hybrid systems use public-key cryptography to exchange secret keys, which are then used in a symmetric-key cryptography systems. Most practical applications of cryptography use a combination of cryptographic functions to implement an overall system that provides all of the four desirable features of secure communications (confidentiality, integrity, authentication, and non-repudiation). === Passwords === Password-authenticated key agreement protocols require the separate establishment of a password (which may be smaller than a key) in a manner that is both private and integrity-assured. These are designed to resist man-in-the-middle and other active attacks on the password and the established keys. For example, DH-EKE, SPEKE, and SRP are password-authenticated variations of Diffie–Hellman. === Other tricks === If one has an integrity-assured way to verify a shared key over a public channel, one may engage in a Diffie–Hellman key exchange to derive a short-term shared key, and then subsequently authenticate that the keys match. One way is to use a voice-authenticated read-out of the key, as in PGPfone. Voice authentication, however, presumes that it is infeasible for a man-in-the-middle to spoof one participant's voice to the other in real-time, which may be an undesirable assumption. Such protocols may be designed to work with even a small public value, such as a password. Variations on this theme have been proposed for Bluetooth pairing protocols. In an attempt to avoid using any additional out-of-band authentication factors, Davies and Price proposed the use of the interlock protocol of Ron Rivest and Adi Shamir, which has been subject to both attack and subsequent refinement.
Data
Data ( DAY-tə, US also DAT-ə, India: DEE-tə) is a collection of discrete or continuous values that conveys information, describing the quantity, quality, fact, statistics, other basic units of meaning, or simply sequences of symbols that may be further interpreted formally. A data point or datum is an individual value in a collection of data. Data is usually organized into structures such as tables that provide additional context and meaning, and may itself be used as data in larger structures. Data may be used as variables in a computational process. Data may represent abstract ideas or concrete measurements. Data is commonly used in scientific research, economics, and virtually every other form of human organizational activity. Examples of data sets include price indices (such as the consumer price index), unemployment rates, literacy rates, and census data. In this context, data represents the raw facts and figures from which useful information can be extracted. Data is collected using techniques such as measurement, observation, query, or analysis, and is typically represented as numbers or characters that may be further processed. Field data is data that is collected in an uncontrolled, in-situ environment. Experimental data is data that is generated in the course of a controlled scientific experiment. Data is analyzed using techniques such as calculation, reasoning, discussion, presentation, visualization, or other forms of post-analysis. Prior to analysis, raw data (or unprocessed data) is typically cleaned: Outliers are removed, and obvious instrument or data entry errors are corrected. Data can be seen as the smallest unit of factual information that can be used as a basis for calculation, reasoning, or discussion. Data can range from abstract ideas to concrete measurements, including, but not limited to, statistics. Thematically connected data presented in some relevant context can be viewed as information. Contextually connected pieces of information can then be described as data insights or intelligence. The stock of insights and intelligence that accumulate over time, resulting from the synthesis of data into information, can then be described as knowledge. Data has been described as "the new oil of the digital economy". Data, as a general concept, refers to the fact that some existing information or knowledge is represented or coded in some form suitable for better usage or processing. Advances in computing technologies have led to the advent of big data, which generally refers to very large quantities of data, typically at the petabyte scale. If restricted to traditional data analysis methods and computing, working with such large (and growing) datasets is difficult, even impossible. In response, the relatively new field of data science uses machine learning (and other artificial intelligence) methods that allow for efficient applications of analytic methods to big data. == Etymology and terminology == The Latin word data is the plural of datum, "(thing) given," and the neuter past participle of dare, "to give". The first English use of the word "data" is from the 1640s. The word "data" was first used to mean "transmissible and storable computer information" in 1946. The expression "data processing" was first used in 1954. When "data" is used more generally as a synonym for "information", it is treated as a mass noun in singular form. This usage is common in everyday language and in technical and scientific fields such as software development and computer science. One example of this usage is the term "big data". When used more specifically to refer to the processing and analysis of sets of data, the term retains its plural form. This usage is common in the natural sciences, life sciences, social sciences, software development and computer science, and grew in popularity in the 20th and 21st centuries. Some style guides do not recognize the different meanings of the term and simply recommend the form that best suits the target audience of the guide. For example, APA style as of the 7th edition requires "data" to be treated as a plural form. == Meaning == Data, information, knowledge, and wisdom are closely related concepts, but each has its role concerning the other, and each term has its meaning. According to a common view, data is collected and analyzed; data only becomes information suitable for making decisions once it has been analyzed in some fashion. One can say that the extent to which a set of data is informative to someone depends on the extent to which it is unexpected by that person. The amount of information contained in a data stream may be characterized by its Shannon entropy. Knowledge is the awareness of its environment that some entity possesses, whereas data merely communicates that knowledge. For example, the entry in a database specifying the height of Mount Everest is a datum that communicates a precisely measured value. This measurement may be included in a book along with other data on Mount Everest to describe the mountain in a manner useful for those who wish to decide on the best method to climb it. Awareness of the characteristics represented by this data is knowledge. Data are often assumed to be the least abstract concept, information the next least, and knowledge the most abstract. In this view, data becomes information by interpretation; e.g., the height of Mount Everest is generally considered "data", a book on Mount Everest geological characteristics may be considered "information", and a climber's guidebook containing practical information on the best way to reach Mount Everest's peak may be considered "knowledge". "Information" bears a diversity of meanings that range from everyday usage to technical use. This view, however, has also been argued to reverse how data emerges from information, and information from knowledge. Generally speaking, the concept of information is closely related to notions of constraint, communication, control, data, form, instruction, knowledge, meaning, mental stimulus, pattern, perception, and representation. Beynon-Davies uses the concept of a sign to differentiate between data and information; data is a series of symbols, while information occurs when the symbols are used to refer to something. Before the development of computing devices and machines, people had to manually collect data and impose patterns on it. With the development of computing devices and machines, these devices can also collect data. In the 2010s, computers were widely used in many fields to collect data and sort or process it, in disciplines ranging from marketing, analysis of social service usage by citizens to scientific research. These patterns in the data are seen as information that can be used to enhance knowledge. These patterns may be interpreted as "truth" (though "truth" can be a subjective concept) and may be authorized as aesthetic and ethical criteria in some disciplines or cultures. Events that leave behind perceivable physical or virtual remains can be traced back through data. Marks are no longer considered data once the link between the mark and observation is broken. Mechanical computing devices are classified according to how they represent data. An analog computer represents a datum as a voltage, distance, position, or other physical quantity. A digital computer represents a piece of data as a sequence of symbols drawn from a fixed alphabet. The most common digital computers use a binary alphabet, that is, an alphabet of two characters typically denoted "0" and "1". More familiar representations, such as numbers or letters, are then constructed from the binary alphabet. Some special forms of data are distinguished. A computer program is a collection of data, that can be interpreted as instructions. Most computer languages make a distinction between programs and the other data on which programs operate, but in some languages, notably Lisp and similar languages, programs are essentially indistinguishable from other data. It is also useful to distinguish metadata, that is, a description of other data. A similar yet earlier term for metadata is "ancillary data." The prototypical example of metadata is the library catalog, which is a description of the contents of books. == Data sources == With respect to ownership of data collected in the course of marketing or other corporate collection, data has been characterized according to party depending on how close the data is to the source or if it has been generated through additional processing. "Zero-party data" refers to data that customers "intentionally and proactively shares". This kind of data can come from a variety of sources, including: subscriptions, preference centers, quizzes, surveys, pop-up forms, and interactive digital experiences. "First-party data" may be collected by a company directly from its customers. The secure exchange of first-party data among companies can be done using data clean rooms. "S
AsoSoft text corpus
The AsoSoft text corpus is the first large-scale Kurdish text corpus, collected and processed by the AsoSoft research and development group. It contains 458,000 documents (188 million tokens) that are collected from sources such as websites, news agencies, books, and magazines. The corpus is partially tagged by topic, so it can be used for topic identification tasks. Also, it is applicable for extracting language model and computational lexicon information. Part of the corpus (75 million tokens) is available online for non-commercial use. The corpus uses the TEI format.
Social bot
A social bot, refers to fully or partially automated social media accounts designed to perform most regular users’ actions, such as liking, posting content, and chatting with other users. Although their levels of autonomy vary, and often include a human-in-the-loop, social bots can use artificial intelligence to perform social media actions and can use large language models to mimic human dialogue. Social bots can operate alone or in groups that coordinate messaging as part of a network of coordinated inauthentic behavior. Social bots are often used to perform ad fraud by artificially boosting viewership and engagement metrics and to spread disinformation on social media. == Uses == Social bots are used for a large number of purposes on a variety of social media platforms, including Twitter, Instagram, Facebook, and YouTube. One common use of social bots is to inflate a social media user's apparent popularity, usually by artificially manipulating their engagement metrics with large volumes of fake likes, reposts, or replies. Social bots can similarly be used to artificially inflate a user's follower count with fake followers, creating a false perception of a larger and more influential online following than is the case. The use of social bots to create the impression of a large social media influence allows individuals, brands, and organizations to attract a higher number of human followers and boost their online presence. Fake engagement can be bought and sold in the black market of social media engagement. Corporations typically use automated customer service agents on social media to affordably manage high levels of support requests. Social bots are used to send automated responses to users’ questions, sometimes prompting the user to private message the support account with additional information. The increased use of automated support bots and virtual assistants has led to some companies laying off customer-service staff. Social bots are also often used to influence public opinion. Autonomous bot accounts can flood social media with large numbers of posts expressing support for certain products, companies, or political campaigns, creating the impression of organic grassroots support. This can create a false perception of the number of people who support a certain position, which may also have effects on the direction of stock prices or on elections. Messages with similar content can also influence fads or trends. Many social bots are also used to amplify phishing attacks. These malicious bots are used to trick a social media user into giving up their passwords or other personal data. This is usually accomplished by posting links claiming to direct users to news articles that would in actuality direct to malicious websites containing malware. Scammers often use URL shortening services such as TinyURL and bit.ly to disguise a link's domain address, increasing the likelihood of a user clicking the malicious link. The presence of fake social media followers and high levels of engagement help convince the victim that the scammer is in fact a trusted user. Social bots can be a tool for computational propaganda. Bots can also be used for algorithmic curation, algorithmic radicalization, and/or influence-for-hire, a term that refers to the selling of an account on social media platforms. == History == Bots have coexisted with computer technology since the earliest days of computing. Social bots have their roots in the 1950s with Alan Turing, whose work focused on machine intelligence with the development of the Turing Test. The following decades saw further progress made towards the goal of creating programs capable of mimicking human behavior, notably with Joseph Weizenbaum’s creation of ELIZA. Considered to be one of the first Chatbots, ELIZA could simulate natural conversations with human users through pattern matching. Its most famous script was DOCTOR, a simulation of a Rogerian psychotherapist that was programmed to chat with patients and respond to questions. With the growth of social media platforms in the early 2000s, these bots could be used to interact with much larger user groups in an inconspicuous manner. Early instances of autonomous agents on social media could be found on sites like MySpace, with social bots being used by marketing firms to inflate activity on a user’s page in an effort to make them appear more popular. Social bots have been observed on a large variety of social media websites, with Twitter being one of the most widely observed examples. The creation of Twitter bots is generally against the site’s terms of service when used to post spam or to automatically like and follow other users, but some degree of automation using Twitter’s API may be permitted if used for “entertainment, informational, or novelty purposes.” Other platforms such as Reddit and Discord also allow for the use of social bots as long as they are not used to violate policies regarding harmful content and abusive behavior. Social media platforms have developed their own automated tools to filter out messages that come from bots, although they cannot detect all bot messages. == Legal regulation == Due to the difficulty of recognizing social bots and separating them from "eligible" automation via social media APIs, it is unclear how legal regulation can be enforced. Social bots are expected to play a role in shaping public opinion by autonomously acting as influencers. Some social bots have been used to rapidly spread misinformation, manipulate stock markets, influence opinion on companies and brands, promote political campaigns, and engage in malicious phishing campaigns. In the United States, some states have started to implement legislation in an attempt to regulate the use of social bots. In 2019, California passed the Bolstering Online Transparency Act (the B.O.T. Act) to make it unlawful to use automated software to appear indistinguishable from humans for the purpose of influencing a social media user's purchasing and voting decisions. Other states such as Utah and Colorado have passed similar bills to restrict the use of social bots. The Artificial Intelligence Act (AI Act) in the European Union is the first comprehensive law governing the use of Artificial Intelligence. The law requires transparency in AI to prevent users from being tricked into believing they are communicating with another human. AI-generated content on social media must be clearly marked as such, preventing social bots from using AI in a manner that mimics human behavior. == Detection == The first generation of bots could sometimes be distinguished from real users by their often superhuman capacities to post messages. Later developments have succeeded in imprinting more "human" activity and behavioral patterns in the agent. With enough bots, it might be even possible to achieve artificial social proof. To unambiguously detect social bots as what they are, a variety of criteria must be applied together using pattern detection techniques, some of which are: cartoon figures as user pictures sometimes also random real user pictures are captured (identity fraud) reposting rate temporal patterns sentiment expression followers-to-friends ratio length of user names variability in (re)posted messages engagement rate (like/followers rate) analysis of the time series of social media posts Social bots are always becoming increasingly difficult to detect and understand. The bots' human-like behavior, ever-changing behavior of the bots, and the sheer volume of bots covering every platform may have been a factor in the challenges of removing them. Social media sites, like Twitter, are among the most affected, with CNBC reporting up to 48 million of the 319 million users (roughly 15%) were bots in 2017. Botometer (formerly BotOrNot) is a public Web service that checks the activity of a Twitter account and gives it a score based on how likely the account is to be a bot. The system leverages over a thousand features. An active method for detecting early spam bots was to set up honeypot accounts that post nonsensical content, which may get reposted (retweeted) by the bots. However, bots evolve quickly, and detection methods have to be updated constantly, because otherwise they may get useless after a few years. One method is the use of Benford's Law for predicting the frequency distribution of significant leading digits to detect malicious bots online. This study was first introduced at the University of Pretoria in 2020. Another method is artificial-intelligence-driven detection. Some of the sub-categories of this type of detection would be active learning loop flow, feature engineering, unsupervised learning, supervised learning, and correlation discovery. Some operations of bots work together in a synchronized way. For example, ISIS used Twitter to amplify its Islamic content by numerous orchestrated accounts which further pushed an item to the Hot List news, thus further a