The P4 metric (also known as FS or Symmetric F ) enables performance evaluation of a binary classifier. The P4 metric is calculated from precision, recall, specificity, and NPV (negative predictive value). The definition of the P4 metric is similar to that of the F1 metric, however the P4 metric definition addresses criticisms leveled against the definition of the F1 metric. The definition of the P4 metric may, therefore, be understood as an extension of the F1 metric. Like the other known metrics, the P4 metric is a function of: TP (true positives), TN (true negatives), FP (false positives), FN (false negatives). == Justification == The key concept of the P4 metric is to leverage the four key conditional probabilities: P ( + ∣ C + ) {\displaystyle P(+\mid C{+})} — the probability that the sample is positive, provided the classifier result was positive. P ( C + ∣ + ) {\displaystyle P(C{+}\mid +)} — the probability that the classifier result will be positive, provided the sample is positive. P ( C − ∣ − ) {\displaystyle P(C{-}\mid -)} — the probability that the classifier result will be negative, provided the sample is negative. P ( − ∣ C − ) {\displaystyle P(-\mid C{-})} — the probability the sample is negative, provided the classifier result was negative. The main assumption behind this metric is that all the probabilities mentioned above are close to 1 for a properly designed binary classifier. Indeed, P 4 = 1 {\displaystyle \mathrm {P} _{4}=1} if, and only if, all of the probabilities above are equal to 1. Another important feature is that P 4 {\displaystyle \mathrm {P} _{4}} tends to zero any of the above probabilities tend to zero. == Definition == P4 is defined as a harmonic mean of four key conditional probabilities: P 4 = 4 1 P ( + ∣ C + ) + 1 P ( C + ∣ + ) + 1 P ( C − ∣ − ) + 1 P ( − ∣ C − ) = 4 1 p r e c i s i o n + 1 r e c a l l + 1 s p e c i f i c i t y + 1 N P V . {\displaystyle \mathrm {P} _{4}={\frac {4}{{\frac {1}{P(+\mid C{+})}}+{\frac {1}{P(C{+}\mid +)}}+{\frac {1}{P(C{-}\mid -)}}+{\frac {1}{P(-\mid C{-})}}}}={\frac {4}{{\frac {1}{\mathit {precision}}}+{\frac {1}{\mathit {recall}}}+{\frac {1}{\mathit {specificity}}}+{\frac {1}{\mathit {NPV}}}}}.} In terms of TP,TN,FP,FN it can be calculated as follows: P 4 = 4 ⋅ T P ⋅ T N 4 ⋅ T P ⋅ T N + ( T P + T N ) ⋅ ( F P + F N ) . {\displaystyle \mathrm {P} _{4}={\frac {4\cdot \mathrm {TP} \cdot \mathrm {TN} }{4\cdot \mathrm {TP} \cdot \mathrm {TN} +(\mathrm {TP} +\mathrm {TN} )\cdot (\mathrm {FP} +\mathrm {FN} )}}.} == Evaluation of the binary classifier performance == Evaluating the performance of binary classifiers is a multidisciplinary concept. It spans from the evaluation of medical tests, psychiatric tests to machine learning classifiers from a variety of fields. Thus, many of the metrics in use exist under several names, some defined independently. == Properties of P4 metric == Symmetry — contrasting to the F1 metric, P4 is symmetrical. It means - it does not change its value when dataset labeling is changed - positives named negatives and negatives named positives. Range: P 4 ∈ [ 0 , 1 ] {\displaystyle \mathrm {P} _{4}\in [0,1]} . Achieving P 4 ≈ 1 {\displaystyle \mathrm {P} _{4}\approx 1} requires all the key four conditional probabilities being close to 1. For P 4 ≈ 0 {\displaystyle \mathrm {P} _{4}\approx 0} it is sufficient that one of the key four conditional probabilities is close to 0. == Examples, comparing with the other metrics == Dependency table for selected metrics ("true" means depends, "false" - does not depend): Metrics that do not depend on a given probability are prone to misrepresentation when the probability approaches 0. === Example 1: Rare disease detection test === Let us consider a medical test used to detect a rare disease. Suppose a population size of 100000 and 0.05% of the population is infected. Further suppose the following test performance: 95% of all positive individuals are classified correctly (TPR=0.95) and 95% of all negative individuals are classified correctly (TNR=0.95). In such a case, due to high population imbalance and in spite of having high test accuracy (0.95), the probability that an individual who has been classified as positive is in fact positive is very low: P ( + ∣ C + ) = 0.0095. {\displaystyle P(+\mid C{+})=0.0095.} We can observe how this low probability is reflected in some of the metrics: P 4 = 0.0370 {\displaystyle \mathrm {P} _{4}=0.0370} , F 1 = 0.0188 {\displaystyle \mathrm {F} _{1}=0.0188} , J = 0.9100 {\displaystyle \mathrm {J} =\mathbf {0.9100} } (Informedness / Youden index), M K = 0.0095 {\displaystyle \mathrm {MK} =0.0095} (Markedness). === Example 2: Image recognition — cats vs dogs === Consider the problem of training a neural network based image classifier with only two types of images: those containing dogs (labeled as 0) and those containing cats (labeled as 1). Thus, the goal is to distinguish between the cats and dogs. Suppose that the classifier overpredicts in favour of cats ("positive" samples): 99.99% of cats are classified correctly and only 1% of dogs are classified correctly. Further, suppose that the image dataset consists of 100000 images, 90% of which are pictures of cats and 10% are pictures of dogs. In this situation, the probability that the picture containing dog will be classified correctly is pretty low: P ( C − | − ) = 0.01. {\displaystyle P(C-|-)=0.01.} Not all metrics are notice this low probability: P 4 = 0.0388 {\displaystyle \mathrm {P} _{4}=0.0388} , F 1 = 0.9478 {\displaystyle \mathrm {F} _{1}=\mathbf {0.9478} } , J = 0.0099 {\displaystyle \mathrm {J} =0.0099} (Informedness / Youden index), M K = 0.8183 {\displaystyle \mathrm {MK} =\mathbf {0.8183} } (Markedness).
Jordan Antiquities Database and Information System
The Jordan Antiquities Database and Information System (JADIS) was a computer database of antiquities in Jordan, the first of its kind in the Arab world. It was established by the Department of Antiquities in 1990, in cooperation with the American Center for Oriental Research in Amman and sponsored by the United States Agency for International Development. JADIS was in use until 2002, when it was superseded by a new system, MEGA-J. Over 10,841 antiquities were registered in the database. An introduction and printed summary of the database was published by the Department of Antiquities in 1994, edited by Gaetano Palumbo.
RR Media
RR Media was a NASDAQ listed provider of global digital media services to the broadcast industry and content owners. Its services can be divided into four main groups: global content distribution network (satellite, fiber and the internet); content management & playout; sports, news & live events; and online video services. The company was rebranded to RR Media from RRsat in September 2014. In February 2016, it was announced that, subject to regulatory approvals, RR Media was to be acquired by SES, based in Betzdorf, Luxembourg, and merged with SES subsidiary company, SES Platform Services a media services provider for television broadcasters, production companies and platform operators, based in Unterföhring near Munich, Germany. In July 2016, the merged company was named MX1. == Digital media services == Global content distribution services RR Media's global distribution network uses a combination of satellite, fiber and the internet. The network includes satellite downlink and uplink; fiber connectivity to digital media hubs; connectivity to TV service providers; and internet-based content delivery. RR Media's network delivers live television channels, streaming media and Video on demand (VOD) content in all formats including Standard-definition television (SD), High-definition television (HD), 4K resolution (4K) & 3D television (3D). End-to-end content management & playout services RR Media manages, prepares and plays out content from its media centers. Services include: content preparation (digitization, localization, conversion, ingest, multiple formatting, editing, restoration); content management (digital asset management, media ingest and library, streamlined workflows, metadata curation, Video on demand (VOD) delivery) and playout, channel creation, playlist management, advertising insertion/management, graphics, titles & overlay, live events operations). RR Media also creates branded or white label product television channels using live and archived materials. Sports, news & live events RR Media delivers live sports and event content for sports rights holders, broadcasters and news channels. Services include: live production (Outside broadcasting vans, Satellite news gathering (SNG), studios), global live distribution, sports content preparation and content management, playout and origination.RR Media provides downlink, uplink, simultaneous translation, turnaround and live production services for sports events like football, basketball, tennis and golf, news and entertainment channels. Online video services RR Media converts existing and archive content into programs, channels and other digital assets, and converges broadcast and internet delivery. Services include converged media (preparing content for broadcast or online use) Content Management Systems (CMS), VOD services, branded platforms, multi-screen delivery, web video portals and viewer measurement tools (using digital analytics). == Media centers == RR Media's media centers are based in Hawley, PA (USA), Emeq Ha’Ela (Israel) Bucharest (Romania), with another facility opened in London, (UK) in June 2015. An additional facility in Miami, FL United States was announced in April 2016. The centers provide RR Media's services, including content preparation, management, online video, live content and distribution, and 24/7 service and support. == Awards == In November 2014, RR Media won the award for Achievement in Legacy Content at the 2014 TVB Europe awards in London, in recognition for its work with British Pathe and the restoration for YouTube. In February 2014, the World Teleport Association named Avi Cohen, CEO of RR Media (formerly RRsat), as its 2014 Teleport Executive of the Year. In 2009, the World Teleport Association awarded RR Media (then RRsat) the Independent Teleport Operator of the Year award for excellence. == History == RR Media (as RRsat) was established in 1981 as a communications provider. The company was founded by David Rivel, an electronics, computers and communications engineer. Rivel is CEO of the company for 31 years and from 2012 a Member of RR Media's board of directors. Under management of Rivel RRsat Communications Network Ltd. went public on 2006-11-01 - NASDAQ:RRST In 2014, the Company rebranded from RRsat Global Communications Network to RR Media. The rebrand was launched at the International Broadcasting Convention (IBC) Show in Amsterdam. In 2015, RR Media announced its NASDAQ stock ticker symbol change to RRM. == Acquisitions == In April 2015, RR Media acquired Eastern Space Systems (ESS) in Romania, a privately held provider of content management and content distribution services and related consulting services. In June 2015, RR Media acquired Satlink Communications as part of strategy to increase scale and expand its global content distribution network and content management footprint, strengthening its customer mix and leverage media industry expertise.
Content creation
Content creation is the act of making and sharing media content, particularly in digital contexts. A content creator is the person or studio behind such content. According to Dictionary.com, content refers to "something that is to be expressed through some medium, as speech, writing or any of various arts" for self-expression, distribution, marketing and/or publication. Content creation encompasses various activities, including maintaining and updating web sites, blogging, article writing, photography, videography, online commentary, social media accounts, and editing and distribution of digital media. In a survey conducted by the Pew Research Center, the content thus created was defined as "the material people contribute to the online world". In addition to traditional forms of content creation, digital platforms face growing challenges related to privacy, copyright, misinformation, platform moderation policies, and the repercussions of violating community guidelines. == Content creators == Content creation is the process of producing and sharing various forms of content such as text, images, audio, and video, designed to engage and inform a specific audience. It plays a crucial role in digital marketing, branding, and online communication and brand awareness. Content can be created for a range of platforms, including social media, websites, blogs, and multimedia channels. Whether it's through written articles, compelling photography, or engaging videos, content creation helps businesses build a connection with their audience, increase visibility, and drive traffic. The process typically involves identifying the target audience, brainstorming ideas, creating the content, and distributing it across various channels. Successful content creation combines creativity with strategic planning, considering audience preferences, trends, and platform characteristics to achieve marketing and branding goals. === News organizations === News organizations, especially those with a large and global reach like The New York Times, NPR, and CNN, consistently create some of the most shared content on the Web, especially in relation to current events. In the words of a 2011 report from the Oxford School for the Study of Journalism and the Reuters Institute for the Study of Journalism, "Mainstream media is the lifeblood of topical social media conversations in the UK." While the rise of digital media has disrupted traditional news outlets, many have adapted and have begun to produce content that is designed to function on the web and be shared on social media. The social media site Twitter is a major distributor and aggregator of breaking news from various sources, and the function and value of Twitter in the distribution of news is a frequent topic of discussion and research in journalism. User-generated content, social media blogging and citizen journalism have changed the nature of news content in recent years. The company Narrative Science is now using artificial intelligence to produce news articles and interpret data. === Colleges, universities, and think tanks === Academic institutions, such as colleges and universities, create content in the form of books, journal articles, white papers, and some forms of digital scholarship, such as blogs that are group edited by academics, class wikis, or video lectures that support a massive open online course (MOOC). Through an open data initiative, institutions may make raw data supporting their experiments or conclusions available on the Web. Academic content may be gathered and made accessible to other academics or the public through publications, databases, libraries, and digital libraries. Academic content may be closed source or open access (OA). Closed-source content is only available to authorized users or subscribers. For example, an important journal or a scholarly database may be a closed source, available only to students and faculty through the institution's library. Open-access articles are open to the public, with the publication and distribution costs shouldered by the institution publishing the content. === Companies === Corporate content includes advertising and public relations content, as well as other types of content produced for profit, including white papers and sponsored research. Advertising can also include auto-generated content, with blocks of content generated by programs or bots for search engine optimization. Companies also create annual reports which are part of their company's workings and a detailed review of their financial year. This gives the stakeholders of the company insight into the company's current and future prospects and direction. === Artists and writers === Cultural works, like music, movies, literature, and art, are also major forms of content. Examples include traditionally published books and e-books as well as self-published books, digital art, fanfiction, and fan art. Independent artists, including authors and musicians, have found commercial success by making their work available on the Internet. === Government === Through digitization, sunshine laws, open records laws and data collection, governments may make statistical, legal or regulatory information available on the Internet. National libraries and state archives turn historical documents, public records, and unique relics into online databases and exhibits. This has raised significant privacy issues. In 2012, The Journal News, a New York state paper, sparked an outcry when it published an interactive map of the state's gun owner locations using legally obtained public records. Governments also create online or digital propaganda or misinformation to support domestic and international goals. This can include astroturfing, or using media to create a false impression of mainstream belief or opinion. Governments can also use open content, such as public records and open data, in service of public health, educational and scientific goals, such as crowdsourcing solutions to complex policy problems. In 2013, the National Aeronautics and Space Administration (NASA) joined the asteroid mining company Planetary Resources to crowdsource the hunt for near-Earth objects. Describing NASA's crowdsourcing work in an interview, technology transfer executive David Locke spoke of the "untapped cognitive surplus that exists in the world" which could be used to help develop NASA technology. In addition to making governments more participatory, open records and open data have the potential to make governments more transparent and less corrupt. === Users === The introduction of Web 2.0 made it possible for content consumers to be more involved in the generation and sharing of content. With the advent of digital media, the amount of user generated content, as well as the age and class range of users, has increased. 8% of Internet users are very active in content creation and consumption. Worldwide, about one in four Internet users are significant content creators, and users in emerging markets lead the world in engagement. Research has also found that young adults of a higher socioeconomic background tend to create more content than those from lower socioeconomic backgrounds. 69% of American and European internet users are "spectators", who consume—but do not create—online and digital media. The ratio of content creators to the amount of content they generate is sometimes referred to as the 1% rule, a rule of thumb that suggests that only 1% of a forum's users create nearly all of its content. Motivations for creating new content may include the desire to gain new knowledge, the possibility of publicity, or simple altruism. Users may also create new content in order to bring about social reforms. However, researchers caution that in order to be effective, context must be considered, a diverse array of people must be included, and all users must participate throughout the process. According to a 2011 study, minorities create content in order to connect with their communities online. African-American users have been found to create content as a means of self-expression that was not previously available. Media portrayals of minorities are sometimes inaccurate and stereotypical which affects the general perception of these minorities. African-Americans respond to their portrayals digitally through the use of social media such as Twitter and Tumblr. The creation of Black Twitter has allowed a community to share their problems and ideas. ==== Teens ==== Younger users now have greater access to content, content creating applications, and the ability to publish to different types of media, such as Facebook, Blogger, Instagram, DeviantArt, or Tumblr. As of 2005, around 21 million teens used the internet and 57%, or 12 million teens, consider themselves content creators. This proportion of media creation and sharing is higher than that of adults. With the advent of the Internet, teens have had more access to tools for sharing an
Algorithmic radicalization
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
Weibo (Chinese: 微博; pinyin: Wēibó), or Sina Weibo (Chinese: 新浪微博; pinyin: Xīnlàng Wēibó), is a Chinese microblogging (weibo) website. Launched by Sina Corporation on 14 August 2009, it is one of the biggest social media platforms in China, with over 582 million monthly active users (252 million daily active users) as of Q1 2022. The platform has been highly successful but has faced criticism for heavy censorship. Sina had gone public on the Nasdaq in 2000. In March 2014, Sina announced a spinoff of Weibo and filed an IPO under the symbol WB. Sina carved out 11% of Weibo in the IPO, with Alibaba owning 32% post-IPO. The company began trading publicly on 17 April 2014. In March 2017, Sina launched Sina Weibo International Version. In November 2018, Sina Weibo suspended its registration function for minors under the age of 14. In July 2019, Sina Weibo announced that it would launch a two-month campaign to clean up pornographic and vulgar information, named "Project Deep Blue" (蔚蓝计划). On 29 September 2020, the company announced it would go private again due to rising tensions between the US and China. == Name == "Weibo" (微博) is the Chinese word for "microblog". Sina Weibo launched its new domain name weibo.com on 7 April 2011, deactivating and redirecting from the old domain, t.sina.com.cn, to the new one. Due to its popularity, the media sometimes refers to the platform simply as "Weibo", despite the numerous other Chinese microblogging services including Tencent Weibo, Sohu Weibo, and NetEase Weibo. However, the latter three have stopped providing services. == Background == Sina Weibo is a platform based on fostering user relationships to share, disseminate, and receive information. Through the website or the mobile app, users can upload pictures and videos publicly for instant sharing, with other users being able to comment with text, pictures and videos, or use a multimedia instant messaging service. The company initially invited a large number of celebrities to join the platform at the beginning and has since invited many media personalities, government departments, businesses and non-governmental organizations to open accounts for the purpose of publishing and communicating information. To avoid the impersonation of celebrities, Sina Weibo uses verification symbols; celebrity accounts have an orange letter "V" and organizations' accounts have a blue letter "V". Sina Weibo has more than 500 million registered users; out of these, 313 million are monthly active users, 85% use the Weibo mobile app, 70% are college-aged, 50.10% are male and 49.90% are female. There are over 100 million messages posted by users each day. With more than 100 million followers, actress Xie Na holds the record for the most followers on the platform. Despite fierce competition among Chinese social media platforms, Sina Weibo remains the most popular. == History == After the July 2009 Ürümqi riots, China shut down most domestic microblogging services, including Fanfou, the very first weibo service. Many popular non-China-based microblogging services like Twitter, Facebook, and Plurk have since been blocked. Sina Corporation CEO Charles Chao considered this to be an opportunity, and on 14 August 2009, Sina launched the tested version of Sina Weibo. Basic functions including message, private message, comment and reposting were made available that September. A Sina Weibo–compatible API platform for developing third-party applications was launched on 28 July 2010. On 1 December 2010, the website experienced an outage, which administrators later said was due to the ever-increasing numbers of users and posts. Registered users surpassed 100 million in February 2011. Since 23 March 2011, t.cn has been used as Sina Weibo's official shortened URL in lieu of sinaurl.cn. On 7 April 2011, weibo.com replaced t.sina.com.cn as the new main domain name used by the website. The official logo was also updated. In June 2011, Sina announced an English-language version of Sina Weibo would be developed and launched, though content would still be governed by Chinese law. On 11 January 2013, Sina Weibo and Alibaba China (a subsidiary of Alibaba Group) signed a strategic cooperation agreement. With more and more foreign celebrities using Sina Weibo, language translation has become an urgent need for Chinese users who wish to communicate with their idols online, especially Korean. In January 2013, Sina Weibo and NetEase.com announced that they had reached a strategic cooperation agreement. When users browse foreign language content, they can now directly obtain translation results through the YouDao Dictionary. The Sina Weibo financial report in February 2013 showed that its total revenue was approximately US$66 million and that the number of registered users had exceeded the 500 million mark. In April 2013, Sina officially announced that Sina Weibo had signed a strategic cooperation agreement with Alibaba. The two sides conducted in-depth cooperation in areas such as user account interoperability, data exchange, online payment, and internet marketing. At the same time, Sina announced that Alibaba, through its wholly owned subsidiary, had purchased the preferred shares and common shares issued by Sina Weibo Company for US$586 million, which accounted for approximately 18% of Weibo's fully diluted and diluted total shares. === Ownership === On 9 April 2013, Alibaba Group announced that it would acquire 18% of Sina Weibo for US$586 million, with the option to buy up to 30% in the future. Alibaba exercised this option when Weibo was listed on the NASDAQ in April 2014. == Users == According to iResearch's report on 30 March 2011, Sina Weibo had 56.5% of China's microblogging market based on active users and 86.6% based on browsing time over competitors such as Tencent Weibo and Baidu. According to research by Sina Corporation, the number of active users reached over 400 million by Q1 2018, making Sina Weibo the 7th platform with at least 400 million active users, and daily usage increased by 21%. As of 2017, approximately 80% of its users were in their 20s and 30s. The top 100 users had over 485 million followers combined. More than 5,000 companies and 2,700 media organizations in China use Sina Weibo. The site is maintained by a growing microblogging department of 200 employees responsible for technology, design, operations, and marketing. Sina executives invited and persuaded many Chinese celebrities to join the platform. Users now include Asian celebrities, movie stars, singers, famous business and media figures, athletes, scholars, artists, organizations, religious figures, government departments, and officials from Hong Kong, Mainland China, Malaysia, Singapore, Taiwan, and Macau, as well as some famous foreign individuals and organizations, including Kevin Rudd, Boris Johnson, David Cameron, Narendra Modi, Toshiba, and the Germany national football team. Sina Weibo has a verification program for known people and organizations. Once an account is verified, a verification badge is added beside the account name. == Features == Many of Sina Weibo's features resemble those of Twitter. A user may post with a 140-character limit (increased to 2,000 as of January 2016 with the exception of reposts and comments). An analysis of 29 million Weibo posts found the median length was 14 characters. Users may mention or talk to other people using "@UserName" formatting, add hashtags, follow other users to make their posts appear in one's own timeline, re-post with "//@UserName" similar to Twitter's retweet function "RT @UserName", select posts for one's favorites list, and verify the account if the user is a celebrity, brand, business or otherwise of public interest. URLs are automatically shortened using the domain name t.cn, akin to Twitter's t.co. Official and third-party applications can access Sina Weibo from other websites or platforms. Users may: Submit up to 18 images/video files in every post Send personal messages to followers Follow others and be followed Post "stories" like on Instagram React to posts using different emojis Receive monetary rewards that can be used in a digital store linked to Weibo View posts identified as "hot" or popular Display the location they post from Hashtags differ slightly between Sina Weibo and Twitter, using the double-hashtag "#HashName#" format (the lack of spacing between Chinese characters necessitates a closing tag). Users can own a hashtag by requesting hashtag monitoring; the company reviews these requests and responds within one to three days. Once a user owns a hashtag, they have access to a wide variety of functions available only to them on the condition that they remain active (less than 1 post per calendar week revokes these privileges). Additionally, comments appear as a list below each post. A commenter can also choose to re-post the comment, quoting the whole original post, to their own page. Unregistered users can only browse a few post
Bridgefy
Bridgefy is a Mexican software company with offices in Mexico and California, the United States, dedicated to developing mesh-networking technology for mobile apps. It was founded circa 2014 by Jorge Rios, Roberto Betancourt and Diego Garcia who conceived the idea while participating in a tech competition called StartupBus. Bridgefy's smartphone ad hoc network technology, apparently using Bluetooth Mesh, is licensed to other apps. The app gained popularity during protests in different countries since it can operate without Internet, using Bluetooth instead. Aware of the security issues of not using cryptography and the criticism surrounding it, Bridgefy announced in late October 2020 that they adopted the Signal protocol, in both their app and SDK, to keep information private, though security researchers have demonstrated that Bridgefy's usage of the Signal Protocol is insecure. == Usage == The app gained popularity as a communication tactic during the 2019–2020 Hong Kong protests and Citizenship Amendment Act protests in India, because it requires people who want to intercept the message to be physically close because of Bluetooth's limited range, and the ability to daisy-chain devices to send messages further than Bluetooth's range. == Security == In August 2020, researchers published a paper describing numerous attacks against the application, which allow de-anonymizing users, building social graphs of users’ interactions (both in real time and after the fact), decrypting and reading direct messages, impersonating users to anyone else on the network, completely shutting down the network, performing active man-in-the-middle attacks to read messages and even modify them. In response to the disclosures, developers acknowledged that "no part of the Bridgefy app is encrypted now" and gave a vague promise to release a new version "encrypted with top security protocols". Later developers said they plan to switch to Signal Protocol, which is widely recognized by cryptographers and used by Signal and WhatsApp. The Signal Protocol was integrated into the Bridgefy app and SDK by late October 2020, with the developers claiming to have included improvements such as the impossibility of a third person impersonating any other user, man-in-the-middle attacks done by modifying stored keys, and historical proximity tracking, among others. However, in 2022, the same security researchers, now including Kenny Paterson, published a paper describing how Bridgefy's usage of the Signal Protocol was incorrect, failing to remedy the previously discovered issues. The researchers performed a demonstration, showing that it was possible for users to intercept messages intended for others without the sender noticing. The researchers disclosed the vulnerabilities to the developers of Bridgefy in August 2021, but, according to the researchers, the developers had yet to resolve the issues as of June 2022. On July 31, 2023, the security firm 7asecurity released a blog post and pentest report of a white box penetration test and overall security review of the Bridgefy app in collaboration with the platform's developers. Their review, which began in November 2022 and concluded in May 2023, identified multiple critical vulnerabilities throughout the application. Many of the issues were fixed, or partially fixed, before the end of the audit, including user impersonation and biometric bypass. Bridgefy also published a blog post on August 8, 2023, announcing the audit results.