23 Matching Annotations
  1. Sep 2023
  2. Aug 2023
    1. The big tech companies, left to their own devices (so to speak), have already had a net negative effect on societies worldwide. At the moment, the three big threats these companies pose – aggressive surveillance, arbitrary suppression of content (the censorship problem), and the subtle manipulation of thoughts, behaviors, votes, purchases, attitudes and beliefs – are unchecked worldwide
      • for: quote, quote - Robert Epstein, quote - search engine bias,quote - future of democracy, quote - tilting elections, quote - progress trap, progress trap, cultural evolution, technology - futures, futures - technology, progress trap, indyweb - support, future - education
      • quote
        • The big tech companies, left to their own devices , have already had a net negative effect on societies worldwide.
        • At the moment, the three big threats these companies pose
          • aggressive surveillance,
          • arbitrary suppression of content,
            • the censorship problem, and
          • the subtle manipulation of
            • thoughts,
            • behaviors,
            • votes,
            • purchases,
            • attitudes and
            • beliefs
          • are unchecked worldwide
      • author: Robert Epstein
        • senior research psychologist at American Institute for Behavioral Research and Technology
      • paraphrase
        • Epstein's organization is building two technologies that assist in combating these problems:
          • passively monitor what big tech companies are showing people online,
          • smart algorithms that will ultimately be able to identify online manipulations in realtime:
            • biased search results,
            • biased search suggestions,
            • biased newsfeeds,
            • platform-generated targeted messages,
            • platform-engineered virality,
            • shadow-banning,
            • email suppression, etc.
        • Tech evolves too quickly to be managed by laws and regulations,
          • but monitoring systems are tech, and they can and will be used to curtail the destructive and dangerous powers of companies like Google and Facebook on an ongoing basis.
      • reference
      • for: titling elections, voting - social media, voting - search engine bias, SEME, search engine manipulation effect, Robert Epstein
      • summary
        • research that shows how search engines can actually bias towards a political candidate in an election and tilt the election in favor of a particular party.
    1. In our early experiments, reported by The Washington Post in March 2013, we discovered that Google’s search engine had the power to shift the percentage of undecided voters supporting a political candidate by a substantial margin without anyone knowing.
      • for: search engine manipulation effect, SEME, voting, voting - bias, voting - manipulation, voting - search engine bias, democracy - search engine bias, quote, quote - Robert Epstein, quote - search engine bias, stats, stats - tilting elections
      • paraphrase
      • quote
        • In our early experiments, reported by The Washington Post in March 2013,
        • we discovered that Google’s search engine had the power to shift the percentage of undecided voters supporting a political candidate by a substantial margin without anyone knowing.
        • 2015 PNAS research on SEME
          • http://www.pnas.org/content/112/33/E4512.full.pdf?with-ds=yes&ref=hackernoon.com
          • stats begin
          • search results favoring one candidate
          • could easily shift the opinions and voting preferences of real voters in real elections by up to 80 percent in some demographic groups
          • with virtually no one knowing they had been manipulated.
          • stats end
          • Worse still, the few people who had noticed that we were showing them biased search results
          • generally shifted even farther in the direction of the bias,
          • so being able to spot favoritism in search results is no protection against it.
          • stats begin
          • Google’s search engine 
            • with or without any deliberate planning by Google employees 
          • was currently determining the outcomes of upwards of 25 percent of the world’s national elections.
          • This is because Google’s search engine lacks an equal-time rule,
            • so it virtually always favors one candidate over another, and that in turn shifts the preferences of undecided voters.
          • Because many elections are very close, shifting the preferences of undecided voters can easily tip the outcome.
          • stats end
    2. he Search Suggestion Effect (SSE), the Answer Bot Effect (ABE), the Targeted Messaging Effect (TME), and the Opinion Matching Effect (OME), among others. Effects like these might now be impacting the opinions, beliefs, attitudes, decisions, purchases and voting preferences of more than two billion people every day.
      • for: search engine bias, google privacy, orwellian, privacy protection, mind control, google bias
      • title: Taming Big Tech: The Case for Monitoring
      • date: May 14th 2018
      • author: Robert Epstein

      • quote

      • paraphrase:
        • types of search engine bias
          • the Search Suggestion Effect (SSE),
          • the Answer Bot Effect (ABE),
          • the Targeted Messaging Effect (TME), and
          • the Opinion Matching Effect (OME), among others. -
        • Effects like these might now be impacting the
          • opinions,
          • beliefs,
          • attitudes,
          • decisions,
          • purchases and
          • voting preferences
        • of more than two billion people every day.
  3. Aug 2022
  4. Jul 2022
  5. www.mojeek.com www.mojeek.com
    1. Mojeek

      Mojeek is the 4th largest English lang. web search engine after Google, Bing and Yandex which has it's own index, crawler and algo. Index has passed 5.7 billion pages. Growing. Privacy based.

      It uses it's own index with no backfill from others.

  6. Apr 2022
    1. How to earn good backlinks for free 2022 afterwardsThe proven ways to earn SEO backlinks at no cost at all - No spamming No harmful software No spend a dimeWhat is backlink? Know the backlink definition from the expert. Find out backlink meaning on Semrush BlogThe proven ways to earn SEO backlinks at no cost at all. No need money to pay SEO agency. No black hat tricks and fast result. More than 300 good domains available as your target link building. To be honest, they are SEO checker, website value estimator, Whois Api sites, Alexa ranks , scam checking tools and so on. They will check and record your site (cached) for different time ranges, sometime recorded permanently and that's your backlinks (short story btw). Most of those sites are partner of Turbo SEO Reviewer network and many thanks to the founder of Turbo SEO organization, we could do more SEO troubleshoots and feel much better with your free SEO tools ;)So, copy paste these domains on your browser, insert your own domain or website onto their test box, click the URLs that link back to your website (if available) to let crawler bot know about a new page that contains your backlink, wait around 3 - 7 days, check your SEO tools dashboard, for example Semrush new backlink monitor or even you can use  your GSC as well (although slower in appearance rather than Semrush).There are several ways to create backlinks for free:1. Check  your site's SEO score on their homepage2. Give good comments properly and then add your link onto form they provide. You may post good comment such as appreciate his articles but don't spam their sites3. Contact the author if the site is not SEO checker and there is no comment box
    1. Open Knowledge Maps, meanwhile, is built on top of the open-source Bielefeld Academic Search Engine, which boasts more than 270 million documents, including preprints, and is curated to remove spam.

      Open Knowledge Maps uses the open-source Bielefeld Academic Search Engine and in 2021 indicated that it covers 270 million documents including preprints. Open Knowledge Maps also curates its index to remove spam.


      How much spam is included in the journal article space? I've heard of incredibly low quality and poorly edited journals, so filtering those out may be fairly easy to do, but are there smaller levels of individual spam below that?

    1. Algospeak refers to code words or turns of phrase users have adopted in an effort to create a brand-safe lexicon that will avoid getting their posts removed or down-ranked by content moderation systems. For instance, in many online videos, it’s common to say “unalive” rather than “dead,” “SA” instead of “sexual assault,” or “spicy eggplant” instead of “vibrator.”

      Definition of "Algospeak"

      In order to get around algorithms that demote content in social media feeds, communities have coined new words or new meanings to existing words to communicate their sentiment.

      This is affecting TikTok in particular because its algorithm is more heavy-handed in what users see. This is also causing people who want to be seen to tailor their content—their speech—to meet the algorithms needs. It is like search engine optimization for speech.

      Article discovered via Cory Doctorow at The "algospeak" dialect

  7. Jan 2022
    1. Budak, C., Soroka, S., Singh, L., Bailey, M., Bode, L., Chawla, N., Davis-Kean, P., Choudhury, M. D., Veaux, R. D., Hahn, U., Jensen, B., Ladd, J., Mneimneh, Z., Pasek, J., Raghunathan, T., Ryan, R., Smith, N. A., Stohr, K., & Traugott, M. (2021). Modeling Considerations for Quantitative Social Science Research Using Social Media Data. PsyArXiv. https://doi.org/10.31234/osf.io/3e2ux

  8. Sep 2021
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  12. Mar 2018
  13. Feb 2017
  14. Jul 2016
  15. Jun 2016
    1. indexer (donc, classer).

      Ok, en fait il s'agit de la catégorisation par l'auteur VS celle effectuée par les moteurs.

  16. May 2015
    1. That is, the human annotators are likely to assign different relevance labels to a document, depending on the quality of the last document they had judged for the same query. In addi- tion to manually assigned labels, we further show that the implicit relevance labels inferred from click logs can also be affected by an- choring bias. Our experiments over the query logs of a commercial search engine suggested that searchers’ interaction with a document can be highly affected by the documents visited immediately be- forehand.