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 the development of radicalized extremist political views. Algorithms meticulously record user interactions, encompassing likes, dislikes and the duration of time watching content, with the objective of generating an endless stream of media designed to sustain user engagement. The phenomenon of echo chamber channels has been demonstrated to exacerbate the polarization of consumers, primarily through the reinforcement of media preferences and the validation of one's existing beliefs. Algorithmic radicalization remains a controversial phenomenon as it is often not in the best interest of social media companies to remove echo chamber channels. To what extent recommender algorithms are actually responsible for radicalization remains disputed. Studies have found contradictory results regarding the promotion of extremist content by algorithms. == Social media echo chambers and filter bubbles == Social media platforms learn the interests and likes of the user to modify their experiences in their feed to keep them engaged and scrolling, known as a filter bubble. An echo chamber is formed when users come across beliefs that magnify or reinforce their thoughts and form a group of like-minded users in a closed system. Echo chambers spread information without any opposing beliefs and can possibly lead to confirmation bias. According to group polarization theory, an echo chamber can potentially lead users and groups towards more extreme radicalized positions. According to the National Library of Medicine, "Users online tend to prefer information adhering to their worldviews, ignore dissenting information, and form polarized groups around shared narratives. Furthermore, when polarization is high, misinformation quickly proliferates." == By site == === Facebook === Facebook's algorithm focuses on recommending content that makes the user want to interact. They rank content by prioritizing popular posts by friends, viral content, and sometimes divisive content. Each feed is personalized to the user's specific interests which can sometimes lead users towards an echo chamber of troublesome content. Users can find their list of interests the algorithm uses by going to the "Your ad Preferences" page. According to a Pew Research study, 74% of Facebook users did not know that list existed until they were directed towards that page in the study. It is also relatively common for Facebook to assign political labels to their users. In recent years, Facebook has started using artificial intelligence to change the content users see in their feed and what is recommended to them. A document known as The Facebook Files has revealed that their AI system prioritizes user engagement over everything else. The Facebook Files has also demonstrated that controlling the AI systems has proven difficult to handle. In an August 2019 internal memo leaked in 2021, Facebook has admitted that "the mechanics of our platforms are not neutral", concluding that in order to reach maximum profits, optimization for engagement is necessary. In order to increase engagement, algorithms have found that hate, misinformation, and politics are instrumental for app activity. As referenced in the memo, "The more incendiary the material, the more it keeps users engaged, the more it is boosted by the algorithm." According to a 2018 study, "false rumors spread faster and wider than true information... They found falsehoods are 70% more likely to be retweeted on Twitter than the truth, and reach their first 1,500 people six times faster. This effect is more pronounced with political news than other categories." === YouTube === YouTube has been around since 2005 and has more than 2.5 billion monthly users. YouTube discovery content systems focus on the user's personal activity (watched, favorites, likes) to direct them to recommended content. YouTube's algorithm is accountable for roughly 70% of users' recommended videos and what drives people to watch certain content. According to a 2022 study by the Mozilla Foundation, users have little power to keep unsolicited videos out of their suggested recommended content. This includes videos about hate speech, livestreams, etc. YouTube has been identified as an influential platform for spreading radicalized content. Al-Qaeda and similar extremist groups have been linked to using YouTube for recruitment videos and engaging with international media outlets. In a research study published by the American Behavioral Scientist Journal, they researched "whether it is possible to identify a set of attributes that may help explain part of the YouTube algorithm's decision-making process". The results of the study showed that YouTube's algorithm recommendations for extremism content factor into the presence of radical keywords in a video's title. In February 2023, in the case of Gonzalez v. Google, the question at hand is whether or not Google, the parent company of YouTube, is protected from lawsuits claiming that the site's algorithms aided terrorists in recommending ISIS videos to users. Section 230 is known to generally protect online platforms from civil liability for the content posted by its users. Multiple studies have found little to no evidence to suggest that YouTube's algorithms direct attention towards far-right content to those not already engaged with it. === TikTok === TikTok is a platform that recommends videos to a user's 'For You Page' (FYP), making every users' page different. With the nature of the algorithm behind the app, TikTok's FYP has been linked to showing more explicit and radical videos over time based on users' previous interactions on the app. Since TikTok's inception, the app has been scrutinized for misinformation and hate speech as those forms of media usually generate more interactions to the algorithm. Various extremist groups, including jihadist organizations, have utilized TikTok to disseminate propaganda, recruit followers, and incite violence. The platform's algorithm, which recommends content based on user engagement, can expose users to extremist content that aligns with their interests or interactions. As of 2022, TikTok's head of US Security has put out a statement that "81,518,334 videos were removed globally between April – June for violating our Community Guidelines or Terms of Service" to cut back on hate speech, harassment, and misinformation. Studies have noted instances where individuals were radicalized through content encountered on TikTok. For example, in early 2023, Austrian authorities thwarted a plot against an LGBTQ+ pride parade that involved two teenagers and a 20-year-old who were inspired by jihadist content on TikTok. The youngest suspect, 14 years old, had been exposed to videos created by Islamist influencers glorifying jihad. These videos led him to further engagement with similar content, eventually resulting in his involvement in planning an attack. Another case involved the arrest of several teenagers in Vienna, Austria, in 2024, who were planning to carry out a terrorist attack at a Taylor Swift concert. The investigation revealed that some of the suspects had been radicalized online, with TikTok being one of the platforms used to disseminate extremist content that influenced their beliefs and actions. == Self-radicalization == The U.S. Department of Justice defines 'Lone-wolf' (self) terrorism as "someone who acts alone in a terrorist attack without the help or encouragement of a government or a terrorist organization". Through social media outlets on the internet, 'Lone-wolf' terrorism has been on the rise, being linked to algorithmic radicalization. Through echo-chambers on the internet, viewpoints typically seen as radical were accepted and quickly adopted by other extremists. These viewpoints are encouraged by forums, group chats, and social media to reinforce their beliefs. == References in media == === The Social Dilemma === The Social Dilemma is a 2020 docudrama about how algorithms behind social media enables addiction, while possessing abilities to manipulate people's views, emotions, and behavior to spread conspiracy theories and disinformation. The film repeatedly uses buzz words such as 'echo chambers' and 'fake news' to prove psychological manipulation on social media, therefore leading to political manipulation. In the film, Ben falls deeper into a social media addiction as the algorithm found that his social media page has a 62.3% chance of long-term engagement. This leads into more videos on the recommended feed for Ben and he eventually becomes more immersed into propaganda and conspiracy theories, becoming more polarized with each video. == Proposed solutions == === United States: Weakening Section 230 protections === In the Communications Decency Act, Section 230 states t
Time series
In mathematics, a time series is a sequence of data points indexed, listed, or graphed in chronological order. Most commonly, a time series consists of observations recorded at successive equally spaced points in time. Thus, it represents a form of discrete-time data. A time series may describe measurements collected over seconds, days, years, or even centuries. Common examples include heights of ocean tides, counts of sunspots, daily temperature readings, and the closing values of stock market indices such as the Dow Jones Industrial Average. A time series is often visualized using a run chart (a type of temporal line chart), which helps identify patterns such as trends, seasonal effects, and irregular fluctuations. Time series are widely used in statistics, actuarial science, signal processing, pattern recognition, econometrics, mathematical finance, weather forecasting, earthquake prediction, electroencephalography, control engineering, astronomy, communications engineering, and many other areas of applied science and engineering that involve temporal measurements. Time series analysis comprises methods for analyzing time series data in order to extract meaningful statistics and other characteristics of the data. Time series forecasting is the use of a model to predict future values based on previously observed values. Generally, time series data is modeled as a stochastic process. While regression analysis is often employed in such a way as to test relationships between one or more different time series, this type of analysis is not usually called "time series analysis", which refers in particular to relationships between different points in time within a single series. Time series data have a natural temporal ordering. This makes time series analysis distinct from cross-sectional studies, in which there is no natural ordering of the observations (e.g. explaining people's wages by reference to their respective education levels, where the individuals' data could be entered in any order). Time series analysis is also distinct from spatial data analysis where the observations typically relate to geographical locations (e.g. accounting for house prices by the location as well as the intrinsic characteristics of the houses). A stochastic model for a time series will generally reflect the fact that observations close together in time will be more closely related than observations further apart. In addition, time series models will often make use of the natural one-way ordering of time so that values for a given period will be expressed as deriving in some way from past values, rather than from future values (see time reversibility). Time series analysis can be applied to real-valued, continuous data, discrete numeric data, or discrete symbolic data (i.e. sequences of characters, such as letters and words in the English language). == Methods for analysis == Methods for time series analysis may be divided into two classes: frequency-domain methods and time-domain methods. The former include spectral analysis and wavelet analysis; the latter include auto-correlation and cross-correlation analysis. In the time domain, correlation and analysis can be made in a filter-like manner using scaled correlation, thereby mitigating the need to operate in the frequency domain. Additionally, time series analysis techniques may be divided into parametric and non-parametric methods. The parametric approaches assume that the underlying stationary stochastic process has a certain structure which can be described using a small number of parameters (for example, using an autoregressive or moving-average model). In these approaches, the task is to estimate the parameters of the model that describes the stochastic process. By contrast, non-parametric approaches explicitly estimate the covariance or the spectrum of the process without assuming that the process has any particular structure. Methods of time series analysis may also be divided into linear and non-linear, and univariate and multivariate. == Panel data == A time series is one type of panel data. Panel data is the general class, a multidimensional data set, whereas a time series data set is a one-dimensional panel (as is a cross-sectional dataset). A data set may exhibit characteristics of both panel data and time series data. One way to tell is to ask what makes one data record unique from the other records. If the answer is the time data field, then this is a time series data set candidate. If determining a unique record requires a time data field and an additional identifier which is unrelated to time (e.g. student ID, stock symbol, country code), then it is panel data candidate. If the differentiation lies on the non-time identifier, then the data set is a cross-sectional data set candidate. == Analysis == There are several types of motivation and data analysis available for time series which are appropriate for different purposes. === Motivation === In the context of statistics, econometrics, quantitative finance, seismology, meteorology, and geophysics the primary goal of time series analysis is forecasting. In the context of signal processing, control engineering and communication engineering it is used for signal detection. Other applications are in data mining, pattern recognition and machine learning, where time series analysis can be used for clustering, classification, query by content, anomaly detection as well as forecasting. === Exploratory analysis === A simple way to examine a regular time series is manually with a line chart. The datagraphic shows tuberculosis deaths in the United States, along with the yearly change and the percentage change from year to year. The total number of deaths declined in every year until the mid-1980s, after which there were occasional increases, often proportionately - but not absolutely - quite large. A study of corporate data analysts found two challenges to exploratory time series analysis: discovering the shape of interesting patterns, and finding an explanation for these patterns. Visual tools that represent time series data as heat map matrices can help overcome these challenges. === Estimation, filtering, and smoothing === This approach may be based on harmonic analysis and filtering of signals in the frequency domain using the Fourier transform, and spectral density estimation. Its development was significantly accelerated during World War II by mathematician Norbert Wiener, electrical engineers Rudolf E. Kálmán, Dennis Gabor and others for filtering signals from noise and predicting signal values at a certain point in time. An equivalent effect may be achieved in the time domain, as in a Kalman filter; see filtering and smoothing for more techniques. Other related techniques include: Autocorrelation analysis to examine serial dependence Spectral analysis to examine cyclic behavior which need not be related to seasonality. For example, sunspot activity varies over 11 year cycles. Other common examples include celestial phenomena, weather patterns, neural activity, commodity prices, and economic activity. Separation into components representing trend, seasonality, slow and fast variation, and cyclical irregularity: see trend estimation and decomposition of time series === Curve fitting === Curve fitting is the process of constructing a curve, or mathematical function, that has the best fit to a series of data points, possibly subject to constraints. Curve fitting can involve either interpolation, where an exact fit to the data is required, or smoothing, in which a "smooth" function is constructed that approximately fits the data. A related topic is regression analysis, which focuses more on questions of statistical inference such as how much uncertainty is present in a curve that is fit to data observed with random errors. Fitted curves can be used as an aid for data visualization, to infer values of a function where no data are available, and to summarize the relationships among two or more variables. Extrapolation refers to the use of a fitted curve beyond the range of the observed data, and is subject to a degree of uncertainty since it may reflect the method used to construct the curve as much as it reflects the observed data. For processes that are expected to generally grow in magnitude one of the curves in the graphic (and many others) can be fitted by estimating their parameters. The construction of economic time series involves the estimation of some components for some dates by interpolation between values ("benchmarks") for earlier and later dates. Interpolation is estimation of an unknown quantity between two known quantities (historical data), or drawing conclusions about missing information from the available information ("reading between the lines"). Interpolation is useful where the data surrounding the missing data is available and its trend, seasonality, and longer-term cycles are known. This is often done by using a relat
Visual networking
Visual networking refers to an emerging class of user applications that combine digital video and social networking capabilities. It is based upon the premise that visual literacy, "the ability to interpret, negotiate and make meaning from information presented in the form of a moving image", is a powerful force in how humans communicate, entertain and learn. The duality of visual networking—subsuming entertainment and communications, professional and personal content, video and other digital media, data networks and social networks to create immersive experiences, when, where and how the user wants it. These applications have changed video content from long-form movies and broadcast television programming to a database of segments or "clips", and social network annotations. And the generation and distribution of content takes on a new dimension with Web 2.0 applications—participatory social-networks or communities that facilitate interactive creativity, collaboration and sharing between users. == History == The rise of visual networking is relatively recent phenomenon driven by the emergence of social networking capabilities and the ability to deliver interactive video over a broadband network. It is a natural evolution of the current social networking phenomena whereby social networking annotations are layered over broadband video to create highly interactive and immersive experiences between individuals and their content. Until early 2005 this was not considered viable due to the lack of web and broadband infrastructure designed to support the transmission of web video and the still nascent stage of social networks like MySpace and Facebook. The introduction of YouTube in February 2005 marked the first significant combination of broadband video and social network systems designed to allow users to share, rate and tag user generated and premium content. From 2006 to 2008 this trend continued to gain steam as individuals and businesses pursued new combinations of video and social networking across a wide range of entertainment, communication and learning applications. == Broadband video takes off == Video has largely been defined by its use as an entertainment medium. Since the commercial availability of the television in the late '30s video has become the dominant entertainment medium far eclipsing audio and text based entertainment both in terms of time and dollars spent. Within the past decade, video use has rapidly evolved across a broader range of devices, multiple locations and user applications. The popularization of the long-tail and user-generated video has further challenged people's ideas of what's possible with video. A key advantage of video relative to other media is its superior ability to communicate ideas and emotions economically. If a picture is worth a thousand words, then a video may be worth a thousand pictures. Video by its very nature is highly experiential, making communications more compelling, informative and memorable. == Social networking meets video == At the core of visual networking is the concept that people can participate in communities of content and communities of interest. A community of interest is defined as a community of people who share a common interest or passion. These people exchange ideas and thoughts about the given passion, but may know (or care) little about each other outside of this area. Participation in a community of interest can be compelling, entertaining and create a ‘sticky’ community where people return frequently and remain for extended periods. The unparalleled potential of the Internet to promote such connections is only now being fully recognized and exploited, through Web-based groups established for that purpose. Based on the six degrees of separation concept (the idea that any two people on the planet could make contact through a chain of no more than five intermediaries), social networking establishes interconnected Internet communities (sometimes known as personal networks) that help people make contacts that would be good for them to know, but that they would be unlikely to have met otherwise. == Transition from search to discovery == The phrase The Long Tail was, according to Chris Anderson, first coined by himself in October 2004. Anderson argued that products that are in low demand or have low sales volume can collectively make up a market share that rivals or exceeds the relatively few current bestsellers and blockbusters, if the store or distribution channel is large enough. The Long Tail also has implications for the producers of content; especially those whose products could not—for economic reasons—find a place in pre-Internet information distribution channels controlled by book publishers, record companies, movie studios, and television networks. Looked at from the producers' side, the Long Tail has made possible a flowering of creativity across all fields of human endeavor. One example of this is YouTube, where thousands of diverse videos—whose content, production value or lack of popularity make them inappropriate for traditional television—are easily accessible to a wide range of viewers. The benefit to the consumer is that they know have an almost infinite choice of content to select from able to create their own specific channels based upon their unique needs. A potential negative side effect of the long tail is the rapidly growing inventory of text, audio and video content. The storage and distribution systems of the past restricted the number of songs, video, and books making it easier to search for what was relevant to the individual. As the long-tail has grown, more and more relevant and irrelevant content passes an individual by without their knowledge. This is especially true for video because unlike text-based files which can searched and indexed for easy finding, video typically has only its title as a clue to what's in it. This lack of comprehensive meta-data has limited the applicability of traditional search models. Augmenting traditional search has been the emergence of content based discovery tools that make people aware of relevant content based upon their participation in communities of interest and/or communities of content. The idea is that users may or may not start out searching for something, but they soon begin reacting to things they find, exploring links on pages they stumble upon and taking cues from fellow surfers about where to go. Instead of the old, passive, lean-back style of watching video, viewers are actively seeking content through discovery. People interact with each other, posting comments on what they just saw. Many sites now allow people to vote on videos, ranking and rating them. Ranking is the result of one of a number of algorithms that measure how many people have watched something or how many sites link to it. == Early examples == YouTube is the best early example of a visual networking experience. YouTube is a video sharing website where users can upload, view and share video clips. Unregistered users can watch most videos on the site, while registered users are permitted to upload an unlimited number of videos. Few statistics are publicly available regarding the number of videos on YouTube. However, in July 2006, the company revealed that more than 100 million videos were being watched every day, and 2.5 billion videos were watched in June 2006. 50,000 videos were being added per day in May 2006, and this increased to 65,000 by July. In January 2008 alone, nearly 79 million users watched over 3 billion videos on YouTube. Telepresence refers to a set of technologies which allow a person to feel as if they were present, to give the appearance that they were present, or to have an effect, at a location other than their true location. Telepresence requires that the senses of the user, or users, are provided with such stimuli as to give the feeling of being in that other location. Additionally, the user(s) may be given the ability to affect the remote location. In this case, the user's position, movements, actions, voice, etc. may be sensed, transmitted and duplicated in the remote location to bring about this effect. Therefore, information may be traveling in both directions between the user and the remote location. Critical the creating an in-person experience is the presence of high-definition video perfectly synchronized with stereophonic sound. A minimum system usually includes visual feedback. Ideally, the entire field of view of the user is filled with a view of the remote location, and the viewpoint corresponds to the movement and orientation of the user's head. In this way, it differs from television or cinema, where the viewpoint is out of the control of the viewer. == Other applications == While still in its infancy, visual networking applications are beginning to emerge that span both consumer and business markets. === Mobile video === Proliferation of multi-function mobile devices, particularl
Media intelligence
Media intelligence uses data mining and data science to analyze public, social and editorial media content. It refers to marketing systems that synthesize billions of online conversations into relevant information. This allow organizations to measure and manage content performance, understand trends, and drive communications and business strategy. Media intelligence can include software as a service using big data terminology. This includes questions about messaging efficiency, share of voice, audience geographical distribution, message amplification, influencer strategy, journalist outreach, creative resonance, and competitor performance in all these areas. Media intelligence differs from business intelligence in that it uses and analyzes data outside company firewalls. Examples of that data are user-generated content on social media sites, blogs, comment fields, and wikis etc. It may also include other public data sources like press releases, news, blogs, legal filings, reviews and job postings. Media intelligence may also include competitive intelligence, wherein information that is gathered from publicly available sources such as social media, press releases, and news announcements are used to better understand the strategies and tactics being deployed by competing businesses. Media intelligence is enhanced by means of emerging technologies like ambient intelligence, machine learning, semantic tagging, natural language processing, sentiment analysis and machine translation. == Technologies used == Different media intelligence platforms use different technologies for monitoring, curating content, engaging with content, data analysis and measurement of communications and marketing campaign success. These technology providers may obtain content by scraping content directly from websites or by connecting to the API provided by social media, or other content platforms that are created for 3rd party developers to develop their own applications and services that access data. Technology companies may also get data from a data reseller. Some social media monitoring and analytics companies use calls to data providers each time an end-user develops a query. Others archive and index social media posts to provide end users with on-demand access to historical data and enable methodologies and technologies leveraging network and relational data. Additional monitoring companies use crawlers and spidering technology to find keyword references, known as semantic analysis or natural language processing. Basic implementation involves curating data from social media on a large scale and analyzing the results to make sense out of it.
Social media and psychology
Social media began in the form of generalized online communities. These online communities formed on websites like Geocities.com in 1994, Theglobe.com in 1995, and Tripod.com in 1995. Many of these early communities focused on social interaction by bringing people together through the use of chat rooms. The chat rooms encouraged users to share personal information, ideas, or even personal web pages. Later the social networking community Classmates took a different approach by simply having people link to each other by using their personal email addresses. By the late 1990s, social networking websites began to develop more advanced features to help users find and manage friends. These newer generation of social networking websites began to flourish with the emergence of SixDegrees.com in 1997, Makeoutclub in 2000, Hub Culture in 2002, and Friendster in 2002. However, the first profitable mass social networking website was the South Korean service, Cyworld. Cyworld initially launched as a blog-based website in 1999 and social networking features were added to the website in 2001. Other social networking websites emerged like Myspace in 2002, LinkedIn in 2003, and Bebo in 2005. In 2009, the social networking website Facebook (launched in 2004) became the largest social networking website in the world. Both Instagram and Kik were launched in October 2010. Active users of Facebook increased from just a million in 2004 to over 750 million by the year 2011. Making internet-based social networking both a cultural and financial phenomenon. In September 2011, Snapchat was launched and reported over 300 million users in 2021. == Psychology of social networking == A social network is a social structure made up of individuals or organizations who communicate and interact with each other. Social networking sites – such as Facebook, Twitter, Instagram, Pinterest and LinkedIn – are defined as technology-enabled tools that assist users with creating and maintaining their relationships. A study found that middle schoolers reported using social media to see what their friends are doing, to post pictures, and to connect with friends. Human behavior related to social networking is influenced by major individual differences, meaning that people differ quite systematically in the quantity and quality of their social relationships. Two of the main personality traits that are responsible for this variability are the traits of extraversion and introversion. Extraversion refers to the tendency to be socially dominant, exert leadership, and influence on others. In contrast, introversion reflects a tendency towards shyness, social phobia, or even avoid social situations altogether, which could potentially reduce the number of social contacts a person may have. These individual differences may result in different social networking outcomes. Other psychology factors related to social media and Media psychology are depression, anxiety, attachment, self-identity, well-being, and the need to belong. === Neuroscience === The three domains that neural systems rely on to be strengthened to support social media use are social cognition, self-referential cognition, and social rewarding. When someone posts something on social media, they think of how their audience will react, while the audience thinks of the motivations behind posting the information. Both parties are analyzing the other's thoughts and feelings, which coherently rely on multiple network systems of the brain including the dorsomedial prefrontal cortex, bilateral temporoparietal junction, anterior temporal lobes, inferior frontal gyri, and posterior cingulate cortex. All of these systems work to help us process social behaviors and thoughts drawn out on social media. Social media requires a great deal of self-referential thought. People use social media as a platform to express their opinions and show off their past and present selves. In other words, as Bailey Parnell said in her Ted Talk, we're showing off our "highlight reel" (4). When one receives feedback from others, the individual obtains more reflected self-appraisal which leads to comparisons of their social behaviors or "highlights" to other users. Self-referential thought involves activity in the medial prefrontal cortex and the posterior cingulate cortex. The brain uses these systems when thinking of oneself. A 2021 umbrella review found that most associations between adolescent social media use and mental health were characterized as weak or inconsistent, though certain studies identified 'substantial' negative impacts, particularly linked to passive consumption and problematic use. Social media also provides a constant supply of rewards that keeps users coming back for more. Whenever users receive a like or a new follower, it activates the brain's social reward system which includes the ventromedial prefrontal cortex, ventral striatum, and ventral tegmental area. This system has been found to activate in response to positive feedback from peers, suggesting that users experience online acceptance in a similar manner to other material rewards or positive experiences, further acting as a potential reward. While these areas of the brain become strengthened, other parts of the brain start to weaken. Technology is encouraging multi-tasking, especially because of how easy it is to switch from one task to another by opening another tab or using two devices at once. The brain's hippocampus is mainly associated with long-term memory. In a study done by Russell Poldark, a professor at UCLA, they found that "for the task learned without distraction, the hippocampus was involved. However, for the task learned with the distraction of the beeps, the hippocampus was not involved; but the striatum was, which is the brain system that underlies our ability to learn new skills." The study concludes that multitasking can cause reliance on the striatum more than the hippocampus, which can change the way we learn. The striatum is known to be connected to mainly the brain's reward system. The brain will strengthen the neurons to the striatum while it weakens the neurons to the hippocampus to make the brain more efficient. Because our brain starts to rely on the striatum more than the hippocampus, it becomes harder for us to process new information. Nicholas Carr, author of The Shallows: How The Internet Is Changing Our Brains, agrees: "What psychologists and brain scientists tell us about interruptions is that they have a fairly profound effect on the way we think. It becomes much harder to sustain attention, to think about one thing for a long period of time, and to think deeply when new stimuli are pouring at you all day long. I argue that the price we pay for being constantly inundated with information is a loss of our ability to be contemplative and to engage in the kind of deep thinking that requires you to concentrate on one thing." === Well-Being === How does well-being relate to social media? In an article titled Social Impact of Psychological Research on Well-Being Shared in Social Media, Pulido et al. found a 15.7% social impact in their results. These new results were compared to a previous study conducted by Pulido et al., which had a high of 4.98% compared to 27.5% in the new study. These results show the ESISM, which is evidence of social impact present. In a two-year span, the difference between social impact rose 22.52% according to these studies. When taking into consideration that an increasingly large number of teens report either being active on, or having used, some form of social media, ranging from apps such as Facebook to TikTok, researching the effects of social media on the well-being of teens and young adults has become more of a topic of focus in recent years. === Depression === Especially in today's society, social media has gained a new perspective on younger generations. It is what younger generations are born into and are growing up to use, particularly what is running today's society. Social Media has its downfalls regarding depression and mental health. Many users often compare their lives regarding what they see on these platforms. In an article Does Social Media Cause Depression? by the Child Mind Institute, Miller states that "several studies, teenage and young adult users who spend the most time on Instagram, Facebook and other platforms for have shown to have substantially (from 13 to 66 percent) higher rates of reported depression than those who spent the least time", what the study shows how Facebook and Instagram, platforms showcasing daily lives and or lifestyles, or less fulfilling or less satisfied or more flaunting base or superficial. Instead of social community, there has become a perception of individuals striving for a life that is not real, whether that is editing photos or making life seem perfect when it is not. This causes a sense of depression by the weight of a comparing game. In "How Social Media Affects Y
ELIZA
ELIZA is an early natural language processing computer program developed from 1964 to 1967 at MIT by Joseph Weizenbaum. Created to explore communication between humans and machines, ELIZA simulated conversation by using a pattern matching and substitution methodology that gave users an illusion of understanding on the part of the program, but gave no response that could be considered really understanding what was being said by either party. Whereas the ELIZA program itself was written (originally) in MAD-SLIP, the pattern matching directives that contained most of its language capability were provided in separate "scripts", represented in a Lisp-like expression. The most famous script, DOCTOR, simulated a psychotherapist of the Rogerian school (in which the therapist often reflects back the patient's words to the patient), and used rules, dictated in the script, to respond with non-directional questions to user inputs. As such, ELIZA was one of the first chatbots (originally "chatterbots") and one of the first programs capable of attempting the Turing test. Weizenbaum intended the program as a method to explore communication between humans and machines. He was surprised that some people, including his secretary, attributed human-like feelings to the computer program, a phenomenon that came to be called the ELIZA effect. Many academics believed that the program would be able to positively influence the lives of many people, particularly those with psychological issues, and that it could aid doctors working on such patients' treatment. While ELIZA was capable of engaging in discourse, it could not converse with true understanding. However, many early users were convinced of ELIZA's intelligence and understanding, despite Weizenbaum's insistence to the contrary. The original ELIZA source code had been missing since its creation in the 1960s, as it was not common to publish articles that included source code at that time. However, more recently the MAD-SLIP source code was discovered in the MIT archives and published on various platforms, such as the Internet Archive. The source code is of high historical interest since it demonstrates not only the specificity of programming languages and techniques at that time, but also the beginning of software layering and abstraction as a means of achieving sophisticated software programming. == Overview == Joseph Weizenbaum's ELIZA, running the DOCTOR script, created a conversational interaction somewhat similar to what might take place in the office of "a [non-directive] psychotherapist in an initial psychiatric interview" and to "demonstrate that the communication between man and machine was superficial". While ELIZA is best known for acting in the manner of a psychotherapist, the speech patterns are due to the data and instructions supplied by the DOCTOR script. ELIZA itself examined the text for keywords, applied values to said keywords, and transformed the input into an output; the script that ELIZA ran determined the keywords, set the values of keywords, and set the rules of transformation for the output. Weizenbaum chose to make the DOCTOR script in the context of psychotherapy to "sidestep the problem of giving the program a data base of real-world knowledge", allowing it to reflect back the patient's statements to carry the conversation forward. The result was a somewhat intelligent-seeming response that reportedly deceived some early users of the program. Weizenbaum named his program ELIZA after Eliza Doolittle, a working-class character in George Bernard Shaw's Pygmalion (also appearing in the musical My Fair Lady, which was based on the play and was hugely popular at the time). According to Weizenbaum, ELIZA's ability to be "incrementally improved" by various users made it similar to Eliza Doolittle, since Eliza Doolittle was taught to speak with an upper-class accent in Shaw's play. However, unlike the human character in Shaw's play, ELIZA is incapable of learning new patterns of speech or new words through interaction alone. Edits must be made directly to ELIZA's active script in order to change the manner by which the program operates. Weizenbaum first implemented ELIZA in his own SLIP list-processing language, where, depending upon the initial entries by the user, the illusion of human intelligence could appear, or be dispelled through several interchanges. Some of ELIZA's responses were so convincing that Weizenbaum and several others have anecdotes of users becoming emotionally attached to the program, occasionally forgetting that they were conversing with a computer. Weizenbaum's own secretary reportedly asked Weizenbaum to leave the room so that she and ELIZA could have a real conversation. Weizenbaum was surprised by this, later writing: "I had not realized ... that extremely short exposures to a relatively simple computer program could induce powerful delusional thinking in quite normal people." In 1966, interactive computing (via a teletype) was new. It was 11 years before the personal computer became familiar to the general public, and three decades before most people encountered attempts at natural language processing in Internet services like Ask.com or PC help systems such as Microsoft Office Clippit. Although those programs included years of research and work, ELIZA remains a milestone because it was the first time a programmer had attempted such a human-machine interaction with the goal of creating the illusion (however brief) of human–human interaction. At the ICCC 1972, ELIZA was brought together with another early artificial-intelligence program named PARRY for a computer-only conversation. While ELIZA was built to speak as a doctor, PARRY was intended to simulate a patient with schizophrenia. == Design and implementation == Weizenbaum originally wrote ELIZA in MAD-SLIP for CTSS on an IBM 7094 as a program to make natural-language conversation possible with a computer. To accomplish this, Weizenbaum identified five "fundamental technical problems" for ELIZA to overcome: the identification of key words, the discovery of a minimal context, the choice of appropriate transformations, the generation of responses in the absence of key words, and the provision of an editing capability for ELIZA scripts. Weizenbaum solved these problems and made ELIZA such that it had no built-in contextual framework or universe of discourse. However, this required ELIZA to have a script of instructions on how to respond to inputs from users. ELIZA starts its process of responding to an input by a user by first examining the text input for a "keyword". A "keyword" is a word designated as important by the acting ELIZA script, which assigns to each keyword a precedence number, or a RANK, designed by the programmer. If such words are found, they are put into a "keystack", with the keyword of the highest RANK at the top. The input sentence is then manipulated and transformed as the rule associated with the keyword of the highest RANK directs. For example, when the DOCTOR script encounters words such as "alike" or "same", it would output a message pertaining to similarity, in this case "In what way?", as these words had high precedence number. This also demonstrates how certain words, as dictated by the script, can be manipulated regardless of contextual considerations, such as switching first-person pronouns and second-person pronouns and vice versa, as these too had high precedence numbers. Such words with high precedence numbers are deemed superior to conversational patterns and are treated independently of contextual patterns. Following the first examination, the next step of the process is to apply an appropriate transformation rule, which includes two parts: the "decomposition rule" and the "reassembly rule". First, the input is reviewed for syntactical patterns in order to establish the minimal context necessary to respond. Using the keywords and other nearby words from the input, different disassembly rules are tested until an appropriate pattern is found. Using the script's rules, the sentence is then "dismantled" and arranged into sections of the component parts as the "decomposition rule for the highest-ranking keyword" dictates. The example that Weizenbaum gives is the input "You are very helpful", which is transformed to "I are very helpful". This is then broken into (1) empty (2) "I" (3) "are" (4) "very helpful". The decomposition rule has broken the phrase into four small segments that contain both the keywords and the information in the sentence. The decomposition rule then designates a particular reassembly rule, or set of reassembly rules, to follow when reconstructing the sentence. The reassembly rule takes the fragments of the input that the decomposition rule had created, rearranges them, and adds in programmed words to create a response. Using Weizenbaum's example previously stated, such a reassembly rule would take the fragments and apply them to the phrase "What makes
Shorty Awards
The Shorty Awards (also known as "The Shortys") are awards for outstanding and innovative work in digital and social media content by brands, advertising agencies, and creators. The awards, which generally focus on short-term content, honor achievements in content creation on Twitter, Facebook, YouTube, Instagram, TikTok, Twitch, and other social networking sites. The Shorty Awards began in 2008 and initially recognized achievements by independent creators on Twitter, with the first formal awards ceremony occurring in February 2009. Since then, the awards, which are now awarded each spring, have shifted their focus to recognize content across numerous platforms. Entrant work is judged on the merits of excellence in creativity, strategy, and engagement by the Real Time Academy, a group of industry professionals selected by the Shorty Awards on the basis of their professional reputations, industry knowledge, and personal achievements (which may include previous Shorty wins). An additional public voting component, known as Audience Honor Voting, is also used to select Shorty Awards contenders. Notable Shorty Award winners include Malala Yousafzai, Trevor Noah, Michelle Obama, Conan O’Brien, Lady Gaga, Bill Nye, Jacob Reed, and Lizzo. Brands and organizations such as Chipotle, Duolingo, Marvel Studios, HBO, Red Bull, Airbnb, Nestle, BMW, UNICEF and the Human Rights Campaign have also been awarded. The Shorty Awards also produces an annual award program called The Shorty Impact Awards, a competition dedicated to showcasing digital and social media-based projects by brands, agencies, and organizations that seek to make the world a better place. == List of ceremonies == == 1st Shorty Awards == The awards were created in 2008 by tech entrepreneurs Greg Galant, Adam Varga, and Lee Semel of Sawhorse Media. They invited Twitter account holders to nominate the best Twitter users in general categories such as humor, news, food, and design. Winners were chosen by more than 30,000 Twitter users during the voting period. The founders of Twitter first heard about the awards after the contest had gotten underway and expressed support for it. The first Shorty Awards ceremony was held on February 11, 2009, at the Galapagos Art Space in Brooklyn, New York. Approximately 300 people attended the event. The event was hosted by CNN anchor Rick Sanchez and featured appearances by prominent Twitter users MC Hammer and Gary Vaynerchuk and a video appearance by Shaquille O'Neal. The awards, in 26 categories, were voted on by Twitter users. == 2nd Shorty Awards == Voting for the second Shorty Awards opened in January 2010 in 26 official categories. A Real-Time Photo of the Year category was added to the list of official categories for the first time, recognizing the best photo posted to services such as Twitpic, Yfrog, or Facebook. The second Shorty Awards competition introduced a panel of judges called the Real-Time Academy of Short Form Arts & Sciences whose members were Craig Newmark, David Pogue, Kurt Andersen, Caterina Fake, Joi Ito, Frank Moss, Alberto Ibargüen, Sreenath Sreenivasan, MC Hammer, Alyssa Milano and Jimmy Wales. After public nominations determined the finalists, the academy decided on the winners. Winners were announced at a ceremony held in the Times Center in The New York Times building in Manhattan that was also streamed online. The ceremony was hosted by CNN anchor Rick Sanchez, who presented awards in the official categories as well as the newly added Real-Time Photo of the Year and a special humanitarian award. == 3rd Shorty Awards == The nomination period for the third annual Shorty Awards opened in January 2011 and ran through February 11, 2011, except for new categories that had extended nomination deadlines. There were 30 official categories and five special categories. In addition to Real-Time Photo of the Year, for the first time the awards accepted nominations for Foursquare Mayor of the Year, Foursquare Location of the Year, Microblog of the Year on Tumblr, and a Connecting People award. The awards also introduced new Shorty Industry Awards to recognize the best uses of social media by brands and agencies. Winners were announced at a ceremony on March 28, 2011, hosted by Aasif Mandvi in the Times Center. Other Shorty Awards presenters were scheduled to include Kiefer Sutherland, Jerry Stiller, Anne Meara, Stephen Wallem, Miss USA Rima Fakih, and Miss Teen USA Kamie Crawford. == 4th Shorty Awards == The 4th Annual Shorty Awards featured Ricky Gervais and Tiffani Thiessen. 1.6 million tweeted nominations were made across all the categories to honor the top users on Twitter, Facebook, Tumblr, Foursquare, YouTube and other internet platforms. == 5th Shorty Awards == The 5th Annual Shorty Awards ceremony featured Felicia Day, James Urbaniak, Kristian Nairn, Hannibal Buress, Carrie Keagan, Chris Hardwick, David Karp and Coco Rocha. 2.4 million tweeted nominations were made across all the categories to honor the top users on Twitter, Facebook, Tumblr, Foursquare, YouTube and other internet sites. == 6th Shorty Awards == The ceremony took place on April 7, 2014, at the New York TimesCenter and was hosted by Comedian Natasha Leggero. The show included appearances by Patton Oswalt, Jamie Oliver, Kristen Bell, Jerry Seinfeld, Moshe Kasher, Julie Klausner, Erin Brady, Guy Kawasaki, Matt Walsh, Retta, Us the Duo, Big Boi, Gilbert Gottfried, Thomas Middleditch, Billie Jean King and Leandra Medine. Winners included Jerry Seinfeld and Will Ferrell. == 7th Shorty Awards == The Seventh Annual Shorty Awards was hosted by comedian Rachel Dratch and took place on April 20, 2015, at The Times Center in NYC. The Real-Time Academy, the judging body of the Shortys, tripled in size for the 7th annual Awards and included Alton Brown, Mamrie Hart, Nikki Glaser, OK Go, The Fine Bros, Debbie Sterling, Dan Savage, Deena Varshavskaya and Palmer Luckey. Panic! at the Disco was the musical guest at the ceremony. On-stage presenters included Kevin Jonas, Bill Nye, Bella Thorne, Wyclef Jean, Emily Kinney and Tyler Oakley. == 8th Shorty Awards == The Eighth Annual Shorty Awards were held in NYC at the TimesCenter on April 11, 2016. They were hosted by YouTuber, Writer and Comedian Mamrie Hart with musical performances from Nico & Vinz. Winners of the night included Bill Wurtz, DJ Khaled, Misty Copeland, Casey Neistat, Dwayne Johnson, Hannah Hart, Troye Sivan, Baddie Winkle, Kevin Hart, Taraji P. Henson, King Bach, and Zach King. == 9th Shorty Awards == The Ninth Annual Shorty Awards were held in NYC at the PlayStation Theater on April 23, 2017. They were hosted by two-time Emmy Award winner Tony Hale with a musical performance by Lizzo. Winners of the night included Bill Nye, Shay Mitchell, Doug the Pug, Gigi Gorgeous, Simone Biles, Mara Wilson, Gaten Matarazzo and Chrissy Teigen. == 10th Shorty Awards == The 10th Annual Shorty Awards, took place on April 15, 2018, at the PlayStation Theater, New York City. The ceremony was hosted by actress, singer, and songwriter Keke Palmer with a musical performance by Betty Who. == 11th Shorty Awards == The 11th Annual Shorty Awards were held on May 5, 2019, in New York City at the PlayStation Theater. The ceremony was hosted by American actress and comedian Kathy Griffin, with a musical performance by Tank and the Bangas. == 12th Shorty Awards == The 12th Annual Shorty Awards were held on May 3, 2020. Due to the COVID-19 pandemic, the ceremony took place online for the first time, with presenters and award winners filming from their own homes. The ceremony was hosted by actor J.B. Smoove and featured a remixed performance of Trap Queen by Fetty Wap. Award winners included Jack Stauber, Supercar Blondie, Rose and Rosie, and Greta Thunberg. == 13th Shorty Awards == The 13th Annual Shorty Awards took place from April 26 to May 14, 2021. The ceremony was hosted on different social media platforms, such as Instagram and Clubhouse, to create a more tailored experience. Winners were announced from May 11 to May 14, with 10 winners being revealed each hour from 1 to 4 p.m. EST on the Shorty Awards Instagram account. == 14th Shorty Awards == The 14th Annual Shorty Awards were held virtually on May 15, 2022, honoring the best in social media and digital content. Hosted by Jay Shetty, the event recognized influencers, brands, and organizations across various categories, celebrating excellence in digital storytelling and innovative online campaigns. Notable winners included Tabitha Brown for her food content and the D'Amelio Family for their contributions to family and parenting content. The event highlighted the role of digital media in connecting and inspiring audiences during challenging times. == 15th Shorty Awards == The 15th Annual Shorty Awards celebrated the best in social media and digital content on May 24, 2023, at Tribeca 360° in New York City. Hosted by Jay Pharoah, the event honored creators, brands, and organizations ac