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Does this also apply to societies at large, i.e., can societies experience mood states that affect their collective decision making? By extension is the public mood correlated or even predictive of economic indicators? Here we investigate whether measurements of collective mood states derived from large-scale Twitter feeds are correlated to the value of the Dow Jones Industrial Average (DJIA) over time. We analyze the text content of daily Twitter feeds by two mood tracking tools, namely OpinionFinder that measures positive vs. negative mood and Google-Profile of Mood States (GPOMS) that measures mood in terms of 6 dimensions (Calm, Alert, Sure, Vital, Kind, and Happy). We cross-validate the resulting mood time series by comparing their ability to detect the public's response to the presidential election and Thanksgiving day in 2008. A Granger causality analysis and a Self-Organizing Fuzzy Neural Network are then used to investigate the hypothesis that public mood states, as measured by the OpinionFinder and GPOMS mood time series, are predictive of changes in DJIA closing values. Our results indicate that the accuracy of DJIA predictions can be significantly improved by the inclusion of specific public mood dimensions but not others. 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This paper addresses the issue on collective intelligence retrieval with activated knowledge-base decision making. Our methodology differs from the existing literature in the sense that we are doing analysis on Twitter microblog messages as opposed to traditional blog analysis in the literature which deals with the conventional 'blogosphere'. Another key difference in our methodology is that we apply visualization techniques in conjunction with artificial intelligence-based data mining methods to classify messages dealing with the trend topic. Our methodology also analyzes demographics of the authors of such Twitter messages and attempt to map a Twitter trend into what's going on in the real world. Our findings reveal a pattern behind trends on Twitter, enabling us to see how it 'ticks' and evolves though visualization methods. 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2009 - Using twitter to recommend real-time topical news.cfm", "md5": "cb9fc7a6d843fd9841fecb02205da9df", "mtime": 1273007732000, "tags": [], "relations": {}, "dateAdded": "2010-05-04T21:15:34Z", "dateModified": "2010-05-04T21:15:36Z" } }, { "key": "M25GG4RP", "version": 1, "library": { "type": "group", "id": 17172, "name": "Social Media Research", "links": { "alternate": { "href": "https://www.zotero.org/groups/social_media_research", "type": "text/html" } } }, "links": { "self": { "href": "https://api.zotero.org/groups/17172/items/M25GG4RP", "type": "application/json" }, "alternate": { "href": "https://www.zotero.org/groups/social_media_research/items/M25GG4RP", "type": "text/html" }, "up": { "href": "https://api.zotero.org/groups/17172/items/5JUH4N76", "type": "application/json" } }, "meta": { "createdByUser": { "id": 235284, "username": "buki", "name": "Andreas Bauer", "links": { "alternate": { "href": "https://www.zotero.org/buki", "type": "text/html" } } } }, "data": { "key": "M25GG4RP", "version": 1, "parentItem": "5JUH4N76", "itemType": "attachment", "linkMode": "imported_url", "title": "ACM Snapshot", "accessDate": "2010-05-04T21:15:35Z", "url": "http://portal.acm.org/citation.cfm?doid=1639714.1639794", "note": "", "contentType": "text/html", "charset": "utf-8", "filename": "citation.html", "md5": null, "mtime": 1273007732000, "tags": [], "relations": {}, "dateAdded": "2010-05-04T21:15:35Z", "dateModified": "2010-05-04T21:15:35Z" } }, { "key": "5JUH4N76", "version": 1, "library": { "type": "group", "id": 17172, "name": "Social Media Research", "links": { "alternate": { "href": "https://www.zotero.org/groups/social_media_research", "type": "text/html" } } }, "links": { "self": { "href": "https://api.zotero.org/groups/17172/items/5JUH4N76", "type": "application/json" }, "alternate": { "href": "https://www.zotero.org/groups/social_media_research/items/5JUH4N76", "type": "text/html" } }, "meta": { "createdByUser": { "id": 235284, "username": "buki", "name": "Andreas Bauer", "links": { "alternate": { "href": "https://www.zotero.org/buki", "type": "text/html" } } }, "creatorSummary": "Phelan et al.", "parsedDate": "2009", "numChildren": 2 }, "data": { "key": "5JUH4N76", "version": 1, "itemType": "conferencePaper", "title": "Using twitter to recommend real-time topical news", "creators": [ { "creatorType": "author", "firstName": "Owen", "lastName": "Phelan" }, { "creatorType": "author", "firstName": "Kevin", "lastName": "McCarthy" }, { "creatorType": "author", "firstName": "Barry", "lastName": "Smyth" } ], "abstractNote": "Recommending news stories to users, based on their preferences, has long been a favourite domain for recommender systems research. In this paper, we describe a novel approach to news recommendation that harnesses real-time micro-blogging activity, from a service such as Twitter, as the basis for promoting news stories from a user's favourite RSS feeds. 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