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1 Social Media Data Mining Becomes Ordinary |
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1 | (18) |
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Data Abundance and Its Consequences |
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1 | (10) |
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Researching Ordinary Social Media Data Mining |
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11 | (8) |
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2 Why Study Social Media Data Mining? |
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19 | (22) |
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19 | (2) |
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Four Characteristics of Social Media: Participation, Sharing, Intimacy and Monetisation |
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21 | (8) |
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What Is Social Media Data Mining? |
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29 | (9) |
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38 | (3) |
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3 What Should Concern Us About Social Media Data Mining? Key Debates |
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41 | (26) |
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41 | (3) |
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Critiques of (Social Media) Data Mining |
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44 | (9) |
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Less Privacy, More Surveillance |
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44 | (3) |
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Discrimination and Control |
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47 | (2) |
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49 | (3) |
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Issues of Access and Inequality |
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52 | (1) |
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Seeking Agency in Data Mining Structures |
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53 | (11) |
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56 | (2) |
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58 | (2) |
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60 | (2) |
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Postscript on Agency: Acting Ethically in Times of Data Mining |
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62 | (2) |
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64 | (3) |
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4 Public Sector Experiments with Social Media Data Mining |
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67 | (32) |
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67 | (3) |
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Action Research and the Production of Publics |
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70 | (6) |
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Knowing and Forming Publics |
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70 | (1) |
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Action-Researching Public Uses of Social Media Data Mining |
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71 | (5) |
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Social Media Data Mining for the Public Good? |
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76 | (11) |
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Uses of Social Media Data Mining |
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76 | (3) |
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Understanding Publics, Desiring Numbers |
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79 | (8) |
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87 | (7) |
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How Keywords Constitute Publics |
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87 | (2) |
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How Expertise (Or Its Absence) Constitutes Publics |
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89 | (2) |
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Working Around Data Non-abundance to Constitute Publics |
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91 | (3) |
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Conclusion: What Should Concern Us About Public Sector Social Media Data Mining? |
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94 | (5) |
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5 Commercial Mediations of Social Media Data |
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99 | (30) |
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99 | (5) |
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The Practices of Intermediary Insights Companies and the Concerns of Workers |
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104 | (7) |
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Who Companies and Interviewees Are |
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104 | (2) |
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What Companies and Interviewees Do |
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106 | (5) |
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Social Media Data Mining as Moral and Economic Practice |
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111 | (13) |
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113 | (3) |
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Drawing Ethical Boundaries |
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116 | (2) |
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118 | (2) |
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Who Benefits from Social Media Data Mining? |
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120 | (2) |
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Regulation as Ethical Solution? |
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122 | (2) |
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Conclusion: Concerns and Ethics in Commercial Social Media Data Mining Practice |
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124 | (5) |
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6 What Happens to Mined Social Media Data? |
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129 | (30) |
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129 | (3) |
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Interviewees, Organisations, and Their Uses of Social Media Data Mining |
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132 | (5) |
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The Consequences of Social Media Data Mining |
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137 | (18) |
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Concrete Action and Organisational Complexity |
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137 | (4) |
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Organisational Change and the Quality of Working Life |
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141 | (3) |
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Data Evangelism and `The Fetishism of the 1000' |
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144 | (7) |
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`The Parasite on the Rhino'? Reflections on Ethical Issues |
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151 | (4) |
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Conclusion: Consequences and Concerns |
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155 | (4) |
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7 Fair Game? User Evaluations of Social Media Data Mining |
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159 | (30) |
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159 | (3) |
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What Do Users Think? Studies of Users' Views |
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162 | (7) |
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Quantitative Studies of Attitudes to Digital Data Tracking |
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162 | (4) |
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Qualitative Studies of Social Media User and Attitudes to Social Media Data Mining, and the `Contextual Integrity' Framework |
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166 | (3) |
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Focus Group Methods for Researching User Perspectives |
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169 | (2) |
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What Do Users Think? Focus Group Findings |
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171 | (14) |
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171 | (8) |
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Common Threads: Concerns About Fairness |
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179 | (6) |
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What Concerns Users? Fairness, Transparency, Contextual Integrity |
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185 | (4) |
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8 Doing Good with Data: Alternative Practices, Elephants in Rooms |
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189 | (32) |
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189 | (2) |
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Elephants in Rooms: Academic Social Media Data Mining |
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191 | (11) |
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Overview of Academic Social Media Data Mining |
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191 | (2) |
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193 | (5) |
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Un-Black-Boxing Social Media Data Mining |
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198 | (4) |
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Alternative Practices: Data Activism |
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202 | (15) |
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202 | (3) |
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Re-active and Pro-active Data Activism |
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205 | (7) |
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Doing Good, Or Doing Bad, Through Data Activism? |
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212 | (5) |
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217 | (4) |
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9 New Data Relations and the Desire for Numbers |
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221 | (16) |
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222 | (2) |
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224 | (6) |
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224 | (1) |
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(Not) Thinking Critically About Data-making |
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225 | (3) |
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228 | (2) |
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230 | (2) |
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232 | (2) |
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234 | (3) |
Bibliography |
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237 | (18) |
Index |
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255 | |