A small tool to view real-world ActivityPub objects as JSON! Enter a URL
or username from Mastodon or a similar service below, and we'll send a
request with
the right
Accept
header
to the server to view the underlying object.
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"content": "<p>The essential feature of knowledge graphs that makes them coproductive with surveillance capitalism is how they allow for a much more fluid means of data integration. Most contemporary corporations are data corporations, and their operation increasingly requires integrating far-flung and heterogeneous datasets, often stitched together from decades of acquisitions. While they are of course not universal, and there is again a large amount of variation in their deployment and use, knowledge graphs power many of the largest information conglomerates. The graph structure of KGs as well as the semantic constraints that can be imposed by controlled ontologies and schemas make them particularly well-suited to the sprawling data conglomerate that typifies contemporary surveillance capitalism.</p><p>I give a case study in RELX, parent of Elsevier and LexisNexis, among others, which is relatively explicit about how it operates as a gigantic graph of data with various overlay platforms. </p><p>/3</p><p><a href=\"https://neuromatch.social/tags/SurveillanceGraphs\" class=\"mention hashtag\" rel=\"tag\">#<span>SurveillanceGraphs</span></a></p>",
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"en": "<p>The essential feature of knowledge graphs that makes them coproductive with surveillance capitalism is how they allow for a much more fluid means of data integration. Most contemporary corporations are data corporations, and their operation increasingly requires integrating far-flung and heterogeneous datasets, often stitched together from decades of acquisitions. While they are of course not universal, and there is again a large amount of variation in their deployment and use, knowledge graphs power many of the largest information conglomerates. The graph structure of KGs as well as the semantic constraints that can be imposed by controlled ontologies and schemas make them particularly well-suited to the sprawling data conglomerate that typifies contemporary surveillance capitalism.</p><p>I give a case study in RELX, parent of Elsevier and LexisNexis, among others, which is relatively explicit about how it operates as a gigantic graph of data with various overlay platforms. </p><p>/3</p><p><a href=\"https://neuromatch.social/tags/SurveillanceGraphs\" class=\"mention hashtag\" rel=\"tag\">#<span>SurveillanceGraphs</span></a></p>"
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"name": "Data companies — most major companies5 — need to store and maintain massive collections of heterogeneous data across their byzantine hierarchies of executives, managers, and workers. This gigantic haunted ball of data is not just a tool, but the substance of the company. A data company persists by exploiting the combinatorics of its data hoard, spinning off new platforms that in turn maintain and expand access to data by creating captive data subjects6. As it expands, a conglomerate will acquire many new sources and modalities of data and need to integrate them with its existing data.\n\nKnowledge graphs are particularly well suited for this “data integration” problem. A full technical description is out of scope here, but briefly: traditional relational database systems can be very difficult to modify and refactor, and that difficulty increases the larger and more complex a database is7. One has to design the structure of the anticipated data in advance, and the abstract schematic structure of the data is embedded in how it is stored and accessed. It is particularly difficult to do unanticipated “long range” analyses where very different kinds of data are analyzed together.\n",
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"name": "In contrast, merging graphs is more straightforward - the data is just triplets, so in an idealized case9 it is possible to just concatenate them and remove duplicates (eg. for a short example, see [35, 36]). The graph can be operated on locally, with more global coordination provided by ontologies and schemas, which themselves have a graph structure [37]. Discrepancies between graphlike schema can be resolved by, you guessed it, making more graph to describe the links and transformations between them. Long-range operations between data are part of the basic structure of a graph - just traverse nodes and edges until you get to where you need to go - and the semantic structure of the graph provides additional constraints to that traversal. Again, a technical description is out of scope here, graphs are not magic, but they are well-suited to merging, modifying, and analyzing large quantities of heterogeneous data10.\n\nSo if you are a data broker, and you just made a hostile acquisition of another data broker who has additional surveillance information to fill the profiles of the people in your existing dataset, you can just stitch those new properties on like a fifth arm on your nightmarish data Frankenstein.",
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"name": "What does this look like in practice? While in a bygone era Elsevier was merely a rentier holding publicly funded research hostage for profit, its parent company RELX is paradigmatic of the transformation of a more traditional information rentier into a sprawling, multimodal surveillance conglomerate (see [38]). RELX proudly describes itself as a gigantic haunted graph of data:\n\n Technology at RELX involves creating actionable insights from big data – large volumes of data in different formats being ingested at high speeds. We take this high-quality data from thousands of sources in varying formats – both structured and unstructured. We then extract the data points from the content, link the data points and enrich them to make it analysable. Finally, we apply advanced statistics and algorithms, such as machine learning and natural language processing, to provide professional customers with the actionable insights they need to do their jobs.\n\n We are continually building new products and data and technology platforms, re-using approaches and technologies across the company to create platforms that are reliable, scalable and secure. Even though we serve different segments with different content sets, the nature of the problems solved and the way we apply technology has commonalities across the company. [39]\n\nAlt text for figure: https://jon-e.net/surveillance-graphs/#in-its-2022-annual-report-relx-describes-its-business-model-as-i",
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"name": "Text from: https://jon-e.net/surveillance-graphs/#derivative-platforms-beget-derivative-platforms-as-each-expands\n\nDerivative platforms beget derivative platforms, as each expands the surface of dependence and provides new opportunities for data to capture. Its integration into clinical systems by way of reference material is growing to include electronic health record (EHR) systems, and they are “developing clinical decision support applications […] leveraging [their] proprietary health graph” [39]. Similarly, their integration into Apple’s watchOS to track medications indicates their interest in directly tracking personal medical data.\n\nThat’s all within biomedical sciences, but RELX’s risk division also provides “comprehensive data, analytics, and decision tools for […] life insurance carriers” [39], so while we will never have the kind of external visibility into its infrastructure to say for certain, it’s not difficult to imagine combining its diverse biomedical knowledge graph with personal medical information in order to sell risk-assessment services to health and life insurance companies. LexisNexis has personal data enough to serve as an “integral part” of the United States Immigration and Customs Enforcement’s (ICE) arrest and deportation program [42, 43], including dragnet location data [44], driving behavior data from internet-connected cars [45], and payment and credit data as just a small sample from its large catalogue [46] [...]",
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