MediaWiki API result

{
    "batchcomplete": "",
    "continue": {
        "gapcontinue": "Recent_or_influential_technical_papers_in_Analysis_of_Social_Media",
        "continue": "gapcontinue||"
    },
    "warnings": {
        "main": {
            "*": "Subscribe to the mediawiki-api-announce mailing list at <https://lists.wikimedia.org/mailman/listinfo/mediawiki-api-announce> for notice of API deprecations and breaking changes."
        },
        "revisions": {
            "*": "Because \"rvslots\" was not specified, a legacy format has been used for the output. This format is deprecated, and in the future the new format will always be used."
        }
    },
    "query": {
        "pages": {
            "910": {
                "pageid": 910,
                "ns": 0,
                "title": "Read the Web Data",
                "revisions": [
                    {
                        "contentformat": "text/x-wiki",
                        "contentmodel": "wikitext",
                        "*": "==Introduction==\nRead the Web is an open IE project led by Tom Mitchell in Carnegie Mellon University. Recently they released some data that could be used in my project.\n\n==Data==\nThere are two types of data publicly available:\n# Knowledge base extracted by [http://rtw.ml.cmu.edu/rtw/ NELL] (Read the Web system)\n#* List of (~440k) beliefs in the KB [http://rtw.ml.cmu.edu/resources/runs/NELL.08m.155.esv.csv.gz download].\n#** Beliefs can be categories or relations. (category example: \"mountain\"; relation example: \"mountaininstate\")\n#** For each belief, relevant contexts are also listed. (e.g., a context for the relation \"mountaininstate\" is \"arg1 mountain range in arg2\")\n#** The confidences of the beliefs were estimated as well.\n#* All contexts learned for each predicate [http://rtw.ml.cmu.edu/resources/runs/NELL.08m.150.extractionPatterns.csv.gz download].\n# Raw data which contains the contexts for all pairs of NPs.\n#* For a pair of NPs, \"people\" and \"hall\", context could be \"arg2 accommodates arg1\".\n\n==Possible Application==\nGiven the context of all NP pairs (data 2), we can try to build a binary classifier to judge if a context represents a potential interesting relation. We can use the context of the confident relations (not categories) appear in data 1 as the positive training samples and combine them with some negative ones. Then we have the data to train the classifier."
                    }
                ]
            },
            "120": {
                "pageid": 120,
                "ns": 0,
                "title": "Reality Mining",
                "revisions": [
                    {
                        "contentformat": "text/x-wiki",
                        "contentmodel": "wikitext",
                        "*": "\n=Definition=\nReality Mining is the collection and analysis of machine-sensed environmental data pertaining to human social behavior, with the goal of identifying predictable patterns of behavior.\n\n=Data Used=\nReality Mining studies human interactions based on the usage of wireless devices such as mobile phones and GPS systems providing a more accurate picture of what people do, where they go, and with whom they communicate with rather than from more subjective sources such as a people's own account.\n\n=Applications: Machine Perception and Learning of Complex Social Systems=\nReality Mining defines the collection of machine-sensed environmental data pertaining to human social behavior. This new paradigm of data mining makes possible the modeling of conversation context, proximity sensing, and temporospatial location throughout large communities of individuals. Mobile phones (and similarly innocuous devices) are used for data collection, opening social network analysis to new methods of empirical stochastic modeling. \nThe original Reality Mining experiment is one of the largest mobile phone projects attempted in academia. Our research agenda takes advantage of the increasingly widespread use of mobile phones to provide insight into the dynamics of both individual and group behavior. By leveraging recent advances in machine learning we are building generative models that can be used to predict what a single user will do next, as well as model behavior of large organizations. \nThey have captured communication, proximity, location, and activity information from 100 subjects at MIT over the course of the 2004-2005 academic year. This data represents over 350,000 hours (~40 years) of continuous data on human behavior. Such rich data on complex social systems have implications for a variety of fields. The research questions we are addressing include:\n* How do social networks evolve over time?\n* How entropic (predictable) are most people's lives?\n* How does information flow?\n* Can the topology of a social network be inferred from only proximity data?\n* How can we change a group's interactions to promote better functioning?"
                    }
                ]
            }
        }
    }
}