Should bad science be censored on social media? (2022, January 19). BBC News. https://www.bbc.com/news/technology-60036861

Carl T. Bergstrom. (2022, January 11). Curious if this what @Twitter meant when they talked about their commitment to combat covid disinformation. Https://t.co/sxrhNVTFW8 [Tweet]. @CT_Bergstrom. https://twitter.com/CT_Bergstrom/status/1480760362496446464

Automated Texting System Saved Lives Weekly During First COVID Surge—Penn Medicine. (n.d.). Retrieved January 10, 2022, from https://www.pennmedicine.org/news/news-releases/2021/november/automated-texting-system-saved-lives-weekly-during-first-covid-surge

Standard algorithms as a reliable engine in SaaS https://en.itpedia.nl/2021/12/06/standaard-algoritmen-als-betrouwbaar-motorblok-in-saas/ The term "Algorithm" has gotten a bad rap in recent years. This is because large tech companies such as Facebook and Google are often accused of threatening our privacy. However, algorithms are an integral part of every application. As is known, SaaS is standard software, which makes use of algorithms just like other software.

• But what are algorithms anyway?
• How can we use standard algorithms?
• How do standard algorithms end up in our software?
• When is software not an algorithm?

Projections are matrix multiplications.Why didn't you sayso? spatial and channel projections in the gated gmlp

The rise of non-algorithmic "algorithms".

Cochrane. (2021, November 10). 🤔 Um, @instagram you got this one wrong! @cochranecollab and @CochraneLibrary continue to be there for those looking to use high-quality information to make #health decisions. Learn more: Https://buff.ly/2R3c82O And search our evidence: Https://buff.ly/2vbkhIJ #infodemic https://t.co/m6NUItZ3tu [Tweet]. @cochranecollab. https://twitter.com/cochranecollab/status/1458439812357185536

Recommended Reading: Amazon’s algorithms, conspiracy theories and extremist literature. (n.d.). ISD. Retrieved November 8, 2021, from https://www.isdglobal.org/isd-publications/recommended-reading-amazons-algorithms-conspiracy-theories-and-extremist-literature/

Clift, Ash K., Carol A. C. Coupland, Ruth H. Keogh, Karla Diaz-Ordaz, Elizabeth Williamson, Ewen M. Harrison, Andrew Hayward, et al. ‘Living Risk Prediction Algorithm (QCOVID) for Risk of Hospital Admission and Mortality from Coronavirus 19 in Adults: National Derivation and Validation Cohort Study’. BMJ 371 (20 October 2020): m3731. https://doi.org/10.1136/bmj.m3731.

Szabelska, A., Pollet, T. V., Dujols, O., Klein, R. A., & IJzerman, H. (2021). A Tutorial for Exploratory Research: An Eight-Step Approach. PsyArXiv. https://doi.org/10.31234/osf.io/cy9mz

Matrajt, Laura, Julia Eaton, Tiffany Leung, und Elizabeth R. Brown. „Vaccine Optimization for COVID-19: Who to Vaccinate First?“ Science Advances 7, Nr. 6 (1. Februar 2020): eabf1374. https://doi.org/10.1126/sciadv.abf1374.

NetScience[@net_science] (2020-08-23) Pseudoscientific Content on YouTube: Assessing the Effects of Watch History on the Recommendation Algorithm. (arXiv:2010.11638v1 [http://cs.CY]) http://arxiv.org/abs/2010.11638. Twitter.Retrieved from: https://twitter.com/SciBeh/status/1319564893503225856

personalize of algorithm can make people spend more money, which help to improve the benifical of both website and merchant.

Pennycook, G., & Rand, D. (2020). The Cognitive Science of Fake News. PsyArXiv. https://doi.org/10.31234/osf.io/ar96c

Kojaku, S., Livan, G., & Masuda, N. (2020). Detecting citation cartels in journal networks. ArXiv:2009.09097 [Physics]. http://arxiv.org/abs/2009.09097

Hotz, J. (n.d.). Can an Algorithm Help Solve Political Paralysis? Scientific American. Retrieved September 21, 2020, from https://www.scientificamerican.com/article/can-an-algorithm-help-solve-political-paralysis/

Shi, W., Wang, L., & Qin, J. (2020). Extracting user influence from ratings and trust for rating prediction in recommendations. Scientific Reports, 10(1), 13592. https://doi.org/10.1038/s41598-020-70350-1

Identifying social media manipulation with OSoMe tools. (2020, August 11). https://www.youtube.com/watch?v=1BMv0PrdVGs&feature=youtu.be

Fischer, E. P., Fischer, M. C., Grass, D., Henrion, I., Warren, W. S., & Westman, E. (2020). Low-cost measurement of facemask efficacy for filtering expelled droplets during speech. Science Advances, eabd3083. https://doi.org/10.1126/sciadv.abd3083

Cazabet, R., Boudebza, S., & Rossetti, G. (2020). Evaluating Community Detection Algorithms for Progressively Evolving Graphs. ArXiv:2007.08635 [Physics]. http://arxiv.org/abs/2007.08635

Ben-David, S. (2018). Clustering—What Both Theoreticians and Practitioners are Doing Wrong. ArXiv:1805.08838 [Cs, Stat]. http://arxiv.org/abs/1805.08838

Kozyreva, A., Lewandowsky, S., & Hertwig, R. (2019, December 4). Citizens Versus the Internet: Confronting Digital Challenges With Cognitive Tools. https://doi.org/10.31234/osf.io/ky4x8

Jazayeri, A., & Yang, C. C. (2020). Motif Discovery Algorithms in Static and Temporal Networks: A Survey. ArXiv:2005.09721 [Physics]. http://arxiv.org/abs/2005.09721

Seetharaman, J. H. and D. (2020, May 26). Facebook Executives Shut Down Efforts to Make the Site Less Divisive. Wall Street Journal. https://www.wsj.com/articles/facebook-knows-it-encourages-division-top-executives-nixed-solutions-11590507499

Costantini, S., De Meo, P., Giorgianni, A., Migliorato, V., Provetti, A., & Salvia, F. (2020). Exploring Low-degree Nodes First Accelerates Network Exploration. ArXiv:2005.08050 [Physics]. http://arxiv.org/abs/2005.08050

Efron, B. (2020). Prediction, Estimation, and Attribution. Journal of the American Statistical Association, 115(530), 636–655. https://doi.org/10.1080/01621459.2020.1762613

Rosenbusch, H., Hilbert, L. P., Evans, A. M., & Zeelenberg, M. (2020). StatBreak: Identifying “Lucky” Data Points Through Genetic Algorithms. Advances in Methods and Practices in Psychological Science, 2515245920917950. https://doi.org/10.1177/2515245920917950

Mariani, M. S., & Lü, L. (2020). Network-based ranking in social systems: Three challenges. Journal of Physics: Complexity, 1(1), 011001. https://doi.org/10.1088/2632-072X/ab8a61

7 emerging themes in digital government. (2020, May 26). https://apolitical.co/en/solution_article/7-emerging-themes-in-digital-government

Lanovaz, M., & Turgeon, S. (2020). Tutorial: Applying Machine Learning in Behavioral Research [Preprint]. PsyArXiv. https://doi.org/10.31234/osf.io/9w6a3

Deepset-ai/haystack. (2020). [Python]. deepset. https://github.com/deepset-ai/haystack (Original work published 2019)

Tuninetti, M., Aleta, A., Paolotti, D., Moreno, Y., & Starnini, M. (2020). Prediction of scientific collaborations through multiplex interaction networks. ArXiv:2005.04432 [Physics]. http://arxiv.org/abs/2005.04432

Nsoesie, E. O., & Oladeji, O. (2020). Identifying patterns to prevent the spread of misinformation during epidemics. Harvard Kennedy School Misinformation Review, COVID-19 and Misinformation. https://doi.org/10.37016/mr-2020-014

Epstein, Z., Pennycook, G., & Rand, D. (2020). Will the Crowd Game the Algorithm? Using Layperson Judgments to Combat Misinformation on Social Media by Downranking Distrusted Sources. Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, 1–11. https://doi.org/10.1145/3313831.3376232

COVIDScholar. (n.d.). Retrieved April 29, 2020, from https://covidscholar.org/

Kerzendorf, W. E., Patat, F., Bordelon, D., van de Ven, G., & Pritchard, T. A. (2020). Distributed peer review enhanced with natural language processing and machine learning. Nature Astronomy. https://doi.org/10.1038/s41550-020-1038-y

Punn, N. S., Sonbhadra, S. K., & Agarwal, S. (2020). COVID-19 Epidemic Analysis using Machine Learning and Deep Learning Algorithms [Preprint]. Health Informatics. https://doi.org/10.1101/2020.04.08.20057679

Captured at http://web.archive.org/web/20160618033503/https://socialmediacollective.org/reading-lists/critical-algorithm-studies/

Là 1 danh sách hướng dẫn chính xác từng bước được dùng để giải quyết vấn đề.

• I wanted to highlight the previous sentence as well, but for some reason it wouldn't let me*

I understand why the author is troubled by the campaign's opinion of "It's not the search engines fault". It makes it seem as if there was nothing that could be done to stop promoting those ideas, and that if something is popular it will just have to be the result at the top.

This can be problematic, as people who were not initially searching that specific phrase may click through to read racist, sexist, homophobic, or biased information (to just name a few) that perpetuates inaccuracies and negative stereotypes. It provides easier access into dangerous thinking built on the foundations of racism, sexism, etc.

If the algorithms are changed or monitored to remove those negative searches, the people exposed to those ideas would decrease, which could help tear down the extreme communities that can build up from them.

While I do understand this view, I also think that system can be helpful too. All the search engine does is reflect the most popular searches, and if negative ideals are what people are searching, then we can become aware and direct their paths to more educational and unbiased sources. It could be interesting to see what would happen if someone clicked on a link that said "Women belong in the kitchen", that led them to results that spoke about equality and feminism.

When Instagram found out that users missed out on 70% of the posts on their feed, they announced a new algorithm. An algorithm that promised to let you see the posts that you care about the most.

<big>评：</big><br/><br/>1933 年德国包豪斯设计学院被纳粹关闭，大部分师生移民到美国，他们同时也把自己的建筑风格带到了美利坚。尽管人们在严格的几何造型上感受到了冷漠感，但是包豪斯主义致力于美术和工业化社会之间的调和，力图探索艺术与技术的新统一，促使公众思考——「如何成为更完备的人」？而这一点间接影响到了我们现在所熟知的美国式人格。<br/><br/>区块链最终会超越「人治」、达到「算法自治」的状态吗？类似的讨论声在人工智能领域同样不绝于耳。「绝对理性」站到了完备人格的对立面，这种冰冷的特质标志着人类与机器交手后的败退。过去有怀疑论者担心，算法的背后实际上由人操控，但随着「由算法生成」的算法，甚至「爷孙代自承袭」算法的出现，这样的担忧逐渐变得苍白无力——我们有了更大的焦虑：是否会出现 “blockchain-based authoritarianism”？

Two core questions in one? As to "how" bad info bubbles to the top of our search results, we know that the algorithms are proprietary—but the humans who design them bring their biases. As to "what is missing," Safiya Noble suggests here and elsewhere that the engineers in Silicon Valley could use a good dose of the humanities and social sciences in their decision-making. Is she right?

Gobo, a proof of concept.

Lines and Distance of point to line

point: P
line_seg_start_point: P0
line_seg_end_point: P1

Would an approach using the Sieve or Eratosthenes work better for very large numbers? Or the best shot would be a probabilistic primality test?

最近点对，算法导论中的经典算法

How can they say student-directed. It's a Facebook algorithm.

Fast Threshold Clustering Algorithm

Looking for equivalent source code to apply in smart content delivery and wireless network optimisation such as Ant Mesh via @KirkDBorne's status https://twitter.com/KirkDBorne/status/479216775410626560 http://cssanalytics.wordpress.com/2013/11/26/fast-threshold-clustering-algorithm-ftca/

I don't totally agree with the fact that writers the creation of language is the target of a writer, I think language is just a means, the "algorithm" that "plays" with words/semanthincs, as any machine can do