In cryptography, the term ciphertext expansion refers to the length increase of a message when it is encrypted. Many modern cryptosystems cause some degree of expansion during the encryption process, for instance when the resulting ciphertext must include a message-unique Initialization Vector (IV). Probabilistic encryption schemes cause ciphertext expansion, as the set of possible ciphertexts is necessarily greater than the set of input plaintexts. Certain schemes, such as Cocks Identity Based Encryption, or the Goldwasser-Micali cryptosystem result in ciphertexts hundreds or thousands of times longer than the plaintext. Ciphertext expansion may be offset or increased by other processes which compress or expand the message, e.g., data compression or error correction coding. == Reasons why Ciphertext expansion can occur == === Probabilistic Encryption === Probabilistic encryption schemes, such as the Goldwasser-Micali cryptosystem, necessarily produce ciphertexts that are longer than the original plaintexts. This is because the set of possible ciphertexts must be larger than the set of plaintexts to achieve semantic security. === Initialization Vectors (IVs) === Many block cipher modes of operation, like Cipher Block Chaining (CBC), require the use of an Initialization Vector (IV) that is unique for each message. The IV is typically appended to the ciphertext, resulting in expansion. === Redundancy and Error Correction === Some cryptographic schemes intentionally introduce redundancy or error correction codes into the ciphertext to protect against tampering or transmission errors. This added data increases the ciphertext size. === Specific Cryptosystems === Certain cryptographic schemes, such as Cocks Identity-Based Encryption, can produce ciphertexts that are hundreds or thousands of times longer than the original plaintext. This extreme expansion is a design choice to achieve the desired security properties. Ciphertext expansion can be offset or increased by other processes that compress or expand the message, such as data compression or error correction coding. The overall impact on message size depends on the relative strengths of these competing effects.
BevQ
BevQ is a queue management mobile application developed by Faircode Technologies of Kochi, Kerala. It is provided by the Kerala State Beverages Corporation under Government of Kerala. == History == This app was released together by the Government of Kerala and the Kerala State Beverages Corporation in order to implement social distancing in the liquor stores Kerala in the case of the COVID-19 pandemic in Kerala and to reduce the congestion of people. The BevQ App was released by Faircode Technologies on 27 May 2020 on the Google Play Store. In January 2021, the app was withdrawn as bars had opened. In June 2021, there was a commitment from the Kerala CM that the App will be relaunched again. It has been reported that over 132,000 new users downloaded the app in the 48 hours after the announcement. == Achievements == The BEVQ app, which works only in the state of Kerala, beat all other Indian food and drink apps in 2020 to see the highest growth in year-on-year sessions, according to the State of Mobile 2021 report by App Annie. The app even beat the likes of Domino’s, which is used all across India. Around 300 government Liquor shops and 900 private liquor shops were enlisted in the platform. More than 200 million unique users registered in the platform. About 250,000 tokens were given out a day.
Algorithmic amplification
Algorithmic amplification is the process by which automated ranking and recommendation systems on digital platforms increase the visibility of certain content beyond its initial audience. Major platforms including Facebook, YouTube, TikTok, and X (formerly Twitter) use such systems to determine what appears in users' feeds and search results. The term is used in research on social media and digital media regulation to describe how platform design choices influence the distribution of online information. Unlike chronological feeds, algorithmic systems evaluate content using signals such as engagement rates, viewing duration, and predicted relevance to individual users. Content that performs strongly on these metrics may be promoted to progressively larger audiences through feeds, search rankings, or autoplay systems. The process is distinct from content moderation, which involves removing, labelling, or restricting content under platform rules, although the two can interact in practice. The concept is closely connected to the attention economy. Research has linked algorithmic amplification to the spread of misinformation and the circulation of political content, as well as to effects on young users' mental health. The scale and direction of those effects remain debated, in part because independent researchers have limited access to the internal workings of platform recommendation systems. Governments in the European Union, United Kingdom, United States, and China have pursued differing regulatory approaches to recommendation algorithms. The EU's Digital Services Act and the UK's Online Safety Act 2023 impose obligations on large platforms related to recommendation system transparency and risk, while China became the first country to enact binding legislation specifically targeting such systems. Internal documents and whistleblower testimony reported by the BBC in 2026 described how competitive pressure between Meta and TikTok led to trade-offs between engagement and user safety in the design of their recommendation systems. == Terminology == The term algorithmic amplification is used in media studies, platform governance scholarship and regulatory literature to describe how automated systems influence the distribution of content beyond what organic user sharing alone would produce. It is distinct from viral spread, which refers primarily to user-driven sharing behaviour, and from algorithmic bias, which describes systematic errors or unfairness in algorithmic outputs. The related term algorithmic curation is used for the broader process of selecting and ordering content, of which amplification is one possible outcome. The phrase also appears in regulatory and legislative discussion of recommendation systems. The European Union's Digital Services Act (DSA) identifies recommendation systems as a potential source of systemic risk, and the term appears frequently in academic and policy commentary on the regulation. In the United States, proposals including the Filter Bubble Transparency Act and the Kids Online Safety Act (KOSA) have used it to frame requirements around recommendation system transparency. In the United Kingdom, the House of Commons Science, Innovation and Technology Committee used the term in a 2025 report on how recommendation algorithms contributed to the spread of misinformation during the 2024 Southport riots. A Joint Declaration on AI and Freedom of Expression adopted in October 2025 by four international freedom of expression mandate holders, including the UN Special Rapporteur on Freedom of Opinion and Expression and the OSCE Representative on Freedom of the Media, stated that recommender systems and other AI-powered curation tools exert "a large hidden influence and gatekeeper role" over what information people access and consume. == Background == Early internet platforms typically displayed content in reverse-chronological order or through keyword-based search systems. Although the term is most often applied to social media, the underlying logic predates social media itself. A 2021 overview traced the origins of modern recommendation systems to the early 1990s, when they were first used experimentally for personal email and information filtering. The 1992 Tapestry mail system and the 1994 GroupLens news filtering system were early milestones before recommendation systems spread into e-commerce and other online services. As user bases and content volumes grew during the 2000s, major platforms including Google, YouTube, and Facebook developed machine-learning systems to personalise content delivery and prioritise material predicted to generate engagement. Facebook introduced its News Feed in 2006, which gradually shifted from chronological presentation towards algorithmically ranked content. YouTube altered its recommendation system in 2012 to prioritise watch time rather than clicks, a change the platform said was prompted by concerns that click-based metrics encouraged misleading thumbnails and low-quality videos. TikTok, launched internationally in 2018, adopted a model in which its primary content surface, the For You feed, is driven almost entirely by algorithmic recommendation rather than by a user's social graph. An internal document obtained by The New York Times in 2021 showed that the platform's algorithm optimised for retention and time spent, using signals such as watch duration, replays, likes, and comments to score and rank videos. Algorithmic recommendation also became central to platforms outside social media. Spotify's personalised features, including Discover Weekly, Release Radar, and Home recommendations, use behavioural signals and inferred "taste profiles" to surface tracks and artists beyond a listener's existing library. An ethnographic study of music curators at streaming platforms described this blend of algorithmic and human editorial selection as an "algo-torial" model of gatekeeping. Amazon adopted item-based collaborative filtering for product recommendations in 1998, and its recommendation engine has been described as one of the earliest large-scale deployments of recommendation technology in e-commerce. The same dynamics operate on adult content platforms. Law professor Amy Adler has argued that from 2007 onwards the pornography industry migrated to algorithm-driven streaming platforms, most of which are controlled by a single near-monopoly company, Aylo (formerly MindGeek). These platforms use algorithmic search engines, suggestions, rigid categorisation of content, and AI-driven search term optimisation in ways that produce the same distorting effects found on mainstream speech platforms, including filter bubbles, feedback loops, and the tendency of algorithmic recommendations to alter individual preferences. == Mechanisms == Recommendation systems commonly combine collaborative filtering, which predicts a user's preferences from the behaviour of similar users, with machine-learning models that predict which content a user is likely to engage with from their prior activity. In a common two-stage design, a platform first generates a set of candidate items from a large content pool and then ranks them using a scoring model with objectives such as predicted engagement or user satisfaction. Small changes in ranking criteria can shift exposure at scale, particularly when applied repeatedly across multiple browsing sessions. These systems typically rely on signals including engagement rates, viewing duration, click-through rates, and network relationships between users. Modern recommendation pipelines continuously update predictions as new behavioural data arrives, allowing platforms to adjust rankings in near real time. Users' revealed preferences, expressed through behaviour such as clicks and viewing time, do not always align with their stated preferences, expressed through explicit feedback such as surveys or content controls. Popularity signals can create feedback dynamics in which early engagement increases the likelihood that content will be shown to additional users. Experimental research on online cultural markets has demonstrated how such feedback processes can produce unequal visibility outcomes even when initial differences in content quality are small. == Beneficial and public-interest uses == Recommendation systems can help users navigate large volumes of content by surfacing material predicted to match their interests or needs, which can improve discoverability on platforms with large content libraries. In public health communication, platforms can help health authorities distribute timely information at scale, though the same recommendation systems also risk amplifying misinformation alongside official guidance. Sociologist Zeynep Tufekci has argued that the shift from independent blogs to large centralised platforms transferred gatekeeping power from traditional media to corporate algorithms. In the case of the Egyptian uprising of 2011, she noted that ordinary users
Digital zombie
A digital zombie is a person so engaged with digital technology or social media they are unable to separate themselves from a persistent online presence. Writing in 2017, University of Sydney researcher Andrew Campbell expressed concerns over whether or not the individual can truly live a full and healthy life while they are preoccupied with the digital world. Other individuals have also begun referencing certain types of behaviour with being a digital zombie. Stefanie Valentic, managing editor of EHS Today, refers to it as people hunting digital creatures through their smartphones in public spaces, always fixed on their phones. The University of Warwick has used the term to argue that further research needs to be done with people who exist in digital form after death to help people grieve their loss. == Modern applications == === Distracted walking === The term digital zombie can refer to a person performing distracted walking, which has been labelled dangerous by the American Academy of Orthopaedic Surgeons. They created the "Digital Deadwalkers" campaign after physicians became aware of the risks associated with walking across intersections and sidewalks while paying attention only to smartphones and not one's surroundings. Also stating that the name is derived from the fact that "they're oblivious to everyone else, so it's like they're dead-walking, sleepwalking." === Living through media === The Department of Sociology, University of Warwick has also identified the term, digital zombie, to refer to an individual who has died but is digitally resurrected, reanimated and socially active. These digital zombies do things in death they did not do when they were alive as they "live" again through a digital self on a digital medium. Dead celebrities sometimes become digital zombies when they are reanimated to appear in commercial advertisements (such as Audrey Hepburn and Bob Monkhouse). Other accidental digital zombies include Tupac Shakur and Michael Jackson who were both digitally resurrected and recreated to perform "live" on stage years after their death. Researchers at the University of Warwick have carried out research into the area of human-computer interaction. in an effort to understand the affect these digital zombies have on grief and bereavement. === Mobile gaming === Writer for EHS Today, Stefanie Valentic, has made observations with the mobile phone video game Pokémon Go, which offers players the experience to hunt and collect digital creatures called Pokémon through their smartphone in real world. Players can be observed simultaneously gazing at their phone while also obliviously walking around their environments looking for Pokémon. Stefanie references these individuals as "digital zombies" since they walk around with no cognition of their surroundings while engaged with their phone. == Health risks == === Heavy use of technology === Research by the University of Sydney has begun looking at how new technology such as digital media and smartphones impact our lives and questioning whether they can create new compulsions and obsessions. The research demonstrates that increased heavy technological use can have negative health consequences similar to drugs, smoking, and alcohol. Marcel O'Gorman, an associate professor of English at the University of Waterloo, has commented on the body of research examining how technology impacts cognition, stating currently that there is no empirical evidence to support any theories that suggest that technology can damage memory and attention span. === Heightened risk to children === Manfred Spitzer, a German psychiatrist, has raised concerns with providing digital devices to children. During the early childhood stage while their brains are rapidly growing, increased exposure to digital devices may deprive them of necessary development required to facilitate brain growth. These concerns are also shared by Korean doctors who believe giving digital devices, like smartphones to children, limits their cognitive development.
Giditraffic
GidiTraffic (or GIDITRAFFIC) is an online social service started on 23 September 2011. Based primarily on social media, the service employs crowdsourcing as its primary means of providing real-time traffic updates to subscribers on its platform. The service, delivered free of charge, affords its users access to various types of information. Though its broadest category of users is road users and motorists, GIDITRAFFIC lends itself as a platform for answering inquiries from anyone who requires information on any subject of interest. GIDITRAFFIC's core competence is in vehicular traffic reports, however, the service also handles all other forms of traffic (going by the fact that the word traffic also means "the mutual exchange of information"). == Operation == Users of the service log on to its Twitter feed to get up-to-date traffic information or to post a general inquiry, which GIDITRAFFIC then publishes to all subscribers. Through crowdsourced replies, a requester receives numerous responses from other subscribers who have seen the question and can provide a relevant answer. In addition, updates are provided by subscribers to the platform via their mobile devices, thereby making the service effective in delivering traffic updates as they occur, and providing timely answers to other user inquiries. This informs GIDITRAFFIC's motto of "Lending each other an eye", alluding to the collaboration and cooperation between the platform's users in making the service indispensable to its users. == Reception == On Twitter, which is its primary platform, the service caters to over 1,800,000 subscribers, with the number increasing daily. The popularity of the platform stems from the fact that it not only keeps its subscribers abreast of the traffic situation in Lagos, the commercial capital city of Nigeria (well known for its many traffic jams), but users in other parts of the world. For a regular user of the platform, knowing where to avoid getting to a set destination in good time is well worth the two or three minutes it takes to access and scroll through the GIDITRAFFIC feed for updates. Another interesting aspect of this platform is the identity of the person behind it. The sustained anonymity of this individual has sparked many discussions centering on his or her possible identity. Online, GIDITRAFFIC continuously publishes traffic updates and user questions, while keeping up witty interactions with the platform's followers round the clock – adding to the mystery and persona of the GIDITRAFFIC owner. == Awards and recognition == In early 2012, GIDITRAFFIC received a nomination for a Shorty Award in the Life-Saving Hero category. Although this did not translate into a win, it brought recognition and wider exposure for the service from international news outlets such as the BBC, Washington Post. and New York Times. Back home in Nigeria, also in 2012, GIDITRAFFIC was honored with a Future Award for Best Use of New Media in recognition of the huge impact the service has had in terms of helping Lagos residents better manage time spent in traffic. == Mobile Applications == In 2012, GIDITRAFFIC partnered with telecommunications company Nokia to produce a downloadable mobile traffic application (the GIDITRAFFIC application, available for Nokia Asha phones on Nokia's online store). There are plans to extend the application to a wider range of mobile phone platforms. On 4 September 2013, the GIDITRAFFIC application for Nokia Lumia phones using Windows Phone 8 was launched on the Windows App Store.
Read Along
Read Along, formerly known as Bolo, is an Android language-learning app for children developed by Google for the Android operating system. The application was released on the Play Store on March 7, 2019. It features a character named Diya helping children learn to read through illustrated stories. It has the facility to learn English and Indian major languages i.e. Hindi, Bengali, Tamil, Telugu, Marathi and Urdu, as well as Spanish, Portuguese and Arabic. == Technology == The app uses text-to-speech technology, through which the character named Dia reads the story, as well as speech-to-text technology, which mechanically identifies the matches between the text and the reading of the user. The story of Chhota Bheem and Katha Kids was added in September 2019. In April 2020, a new version of the application was released. In September 2020, it added Arabic language to its language option. A web version was launched in August 2022.
Directed-energy weapon wildfire conspiracy theories
The directed-energy weapon wildfire conspiracy theories are claims circulating on social media and in fringe commentary that 2020s wildfires in places such as California, Hawaii and Texas were started or steered by directed-energy weapons or other lasers or directed-energy systems rather than by the documented ignition sources identified by investigators. Fact-checking organisations and newsrooms have repeatedly shown that widely shared images and clips said to depict “beams from the sky” are unrelated, miscaptioned or fabricated, and that official inquiries point to causes such as damaged or re-energised power lines, vegetation and extreme wind conditions. Coverage of the January 2025 Los Angeles fires described a resurgence of familiar hoaxes while local and federal agencies coordinated public rebuttals. == Background == Rumours linking directed-energy weapons to wildfire outbreaks appeared during earlier disaster seasons, then re-emerged at scale during the 2018 Camp Fire and again with the 2023 Maui wildfires and the 2025 Los Angeles fires. Journalists documented how large disasters reliably attract miscaptioned imagery and speculative narratives that portray official explanations as cover stories, while researchers and emergency managers noted that such claims tend to flourish during the information vacuum that accompanies fast-moving events. == Narratives and debunks == Recurring claims include assertions that videos show lasers igniting neighbourhoods, that “green” or “blue” items or roofs were spared because lasers cannot burn those colours, that trees remaining upright indicate precision targeting of houses, and that beams recorded over Hawaii or Texas came from secret platforms. Investigations show that a purported laser-strike video was actually an explosion at a Russian gas station recorded years earlier, that a photograph said to capture an “attack” was an Ohio gas flare from 2018, and that a separate video of green lights over Hawaii was captured months before the Maui fires by an astronomical camera and is unrelated. Fact-checks addressing colour myths have further explained that images of intact blue roofs were either misinterpreted or in at least one widely shared instance artificially generated, and that laser interaction with materials is not governed by such simplistic rules. == Investigations and identified causes == Authorities who examined specific incidents have published findings that contradict DEW narratives. A multi-agency investigation into the Maui disaster concluded that downed and later re-energised lines ignited an initial morning fire that re-kindled under extreme winds in the afternoon, with reports detailing the timeline and infrastructure context; summaries by national outlets echoed those conclusions. Investigators of the February 2024 Smokehouse Creek Fire in the Texas Panhandle reported that power lines ignited both the state’s largest wildfire and another major blaze, and the regional utility acknowledged its facilities appeared to have been involved; subsequent media coverage outlined the findings and regulatory follow-up. For the 2018 Camp Fire in Northern California, public reports from Butte County and subsequent proceedings identified PG&E transmission equipment as the source of ignition, with documentation of maintenance issues on the Caribou–Palermo line preceding the event. == Platform and agency responses == As major fires burned in and around Los Angeles in January 2025, officials from city agencies and national partners pursued a coordinated strategy to counter falsehoods by issuing timely updates, flagging fake imagery and directing residents to verified resources. Reporters described how federal emergency managers and local departments used social channels and briefings to rebut specific rumours, including claims about lasers and targeted ignition, and to clarify that early imagery often misleads during fast-moving disasters.