├── .gitignore ├── README.md ├── Tutorial.ipynb ├── data ├── SMH │ ├── SMH-cascade-0.csv │ ├── SMH-cascade-1.csv │ ├── SMH-cascade-10.csv │ ├── SMH-cascade-11.csv │ ├── SMH-cascade-12.csv │ ├── SMH-cascade-13.csv │ ├── SMH-cascade-14.csv │ ├── SMH-cascade-15.csv │ ├── SMH-cascade-16.csv │ ├── SMH-cascade-17.csv │ ├── SMH-cascade-18.csv │ ├── SMH-cascade-19.csv │ ├── SMH-cascade-2.csv │ ├── SMH-cascade-3.csv │ ├── SMH-cascade-4.csv │ ├── SMH-cascade-5.csv │ ├── SMH-cascade-6.csv │ ├── SMH-cascade-7.csv │ ├── SMH-cascade-8.csv │ └── SMH-cascade-9.csv └── hashtags-political-bias.xlsx └── scripts ├── __init__.py ├── __pycache__ ├── __init__.cpython-36.pyc ├── influence.cpython-36.pyc └── user_influence.cpython-36.pyc ├── casIn ├── __init__.py ├── __pycache__ │ ├── __init__.cpython-36.pyc │ └── user_influence.cpython-36.pyc └── user_influence.py └── influence.py /.gitignore: -------------------------------------------------------------------------------- 1 | .ipynb_checkpoints/ 2 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | 2 | # Cascade Influence 3 | 4 | This repository contains: 5 | 6 | - The scripts to estimate user influence from Twitter information cascades (i.e. Cas.In); 7 | - A small dataset of 20 cascades for testing Cas.In; 8 | - A hands-on tutorial to walk you through running Cas.In on real cascades. 9 | 10 | ### Citation 11 | The algorithm was introduced in the paper: 12 | 13 | Rizoiu, M.-A., Graham, T., Zhang, R., Zhang, Y., Ackland, R., & Xie, L. (2018). **#DebateNight: The Role and Influence of Socialbots on Twitter During the 1st 2016 U.S. Presidential Debate**. In *Proc. International AAAI Conference on Web and Social Media (ICWSM ’18)* (pp. 1–10). Stanford, CA, USA. 14 | [pdf at arxiv with supplementary material](https://arxiv.org/abs/1802.09808) 15 | 16 | **Bibtex** 17 | ``` 18 | @inproceedings{rizoiu2018debatenight, 19 | address = {Stanford, CA, USA}, 20 | author = {Rizoiu, Marian-Andrei and Graham, Timothy and Zhang, Rui and Zhang, Yifei and Ackland, Robert and Xie, Lexing}, 21 | booktitle = {International AAAI Conference on Web and Social Media (ICWSM '18)}, 22 | title = {{{\#}DebateNight: The Role and Influence of Socialbots on Twitter During the 1st 2016 U.S. Presidential Debate}}, 23 | url = {https://arxiv.org/abs/1802.09808}, 24 | year = {2018} 25 | } 26 | ``` 27 | 28 | ### License 29 | Both dataset and code are distributed under the Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) license, a copy of which can be obtained following this link. If you require a different license, please contact [Yifei Zhang](mailto:yifeiacc@gmail.com), [Marian-Andrei Rizoiu](mailto:Marian-Andrei@rizoiu.eu) or [Lexing Xie](mailto:Lexing.Xie@anu.edu.au). 30 | 31 | # How to run Cas.In in a terminal: 32 | 33 | ### Required packages: 34 | 35 | - python3 36 | - numpy 37 | - pandas 38 | 39 | ### Arguments of Cas.In: 40 | 41 | *--cascade_path* : the path of cascade file (see the format here below). 42 | 43 | *--time_decay* : the coefficient value of time decay (hyperparameter $r$ in the paper). **Default**:-0.000068 44 | 45 | *--save2csv* : save result to csv file. **Default**: False 46 | 47 | ### Command: 48 | ```bash 49 | cd scripts 50 | python3 influence.py --cascade_path path/to/file 51 | ``` 52 | 53 | # File format and toy dataset 54 | 55 | ### Dataset 56 | We provide a toy dataset -- dubbed SMH -- for testing Cas.In. 57 | It was collected in 2017 by following the Twitter handle of the Sydney Morning Herald newspaper (tweets and retweets mentioning SMH or linking to an article from SMH). 58 | 59 | The data contains 20 cascades (one file per cascade). 60 | We annonymized the `user_id` (as per Twitter's ToS) by mapping original values to a sequence from 0 to n, while preserving the identity of users across cascades. 61 | 62 | ### The format cascade files: 63 | - A csv file with 3 columns (`time`, `magnitude`, `user_id`), where each row is a tweet in the cascade: 64 | - `time` represents the timestamp of tweet -- the first tweet is always at time zero, for the following retweets it shows the offset in seconds from the initial tweet; 65 | - `magnitude` is the local influence of the user (here the number of followers); 66 | - `user_id` the id of the user emitting the tweet (here annonymized). 67 | - The rows in the file (i.e. the tweets) are sorted by the timestamp; 68 | 69 | eg: 70 | ``` 71 | time,magnitude,user_id 72 | 0,4674,"0" 73 | 321,1327,"1" 74 | 339,976,"2" 75 | 383,477,"3" 76 | 699,1209,"4" 77 | 824,119,"5" 78 | 835,1408,"6" 79 | 1049,896,"7" 80 | ``` 81 | 82 | # Cascade influence tutorial 83 | 84 | Next, we drive you through using Cas.In for estimating user influence starting from a single cascade. 85 | 86 | ### Preliminary 87 | We need to first load all required packages of cascade influence. 88 | 89 | 90 | ```python 91 | cd scripts 92 | ``` 93 | 94 | ```python 95 | import pandas as pd 96 | import numpy as np 97 | from casIn.user_influence import P,influence 98 | ``` 99 | 100 | ## Compute influence in one cascade 101 | 102 | ### Read data 103 | Load the first cascade in the SMH toy dataset: 104 | 105 | 106 | ```python 107 | cascade = pd.read_csv("../data/SMH/SMH-cascade-0.csv") 108 | cascade.head() 109 | ``` 110 | 111 | 112 | 113 | 114 |
115 | 116 | 117 | 118 | 119 | 120 | 121 | 122 | 123 | 124 | 125 | 126 | 127 | 128 | 129 | 130 | 131 | 132 | 133 | 134 | 135 | 136 | 137 | 138 | 139 | 140 | 141 | 142 | 143 | 144 | 145 | 146 | 147 | 148 | 149 | 150 | 151 | 152 | 153 | 154 | 155 | 156 |
timemagnitudeuser_id
00991419
11271352658
221492057264
3246511551016
424851917790
157 |
158 | 159 | 160 | 161 | ### Compute matrix P 162 | 163 | We first need to compute the probabilities ![$p_{ij}$](http://latex.codecogs.com/gif.latex?%24p_%7Bij%7D%24), where ![$p_{ij}$](http://latex.codecogs.com/gif.latex?%24p_%7Bij%7D%24) is the probability that ![$j^{th}$](http://latex.codecogs.com/gif.latex?%24j%5E%7Bth%7D%24) tweet is a direct retweet of the ![$i^{th}$](http://latex.codecogs.com/gif.latex?%24i%5E%7Bth%7D%24) (see the paper for more details). 164 | We need to specify the hyper-parameter ![$r$](http://latex.codecogs.com/gif.latex?%24r%24), the time decay coefficient. 165 | Here we choose ![$r = -0.000068$](http://latex.codecogs.com/gif.latex?%24r%20%3D%20-0.000068%24). 166 | 167 | 168 | ```python 169 | p_ij = P(cascade,r = -0.000068) 170 | ``` 171 | 172 | ### Compute user influence and matrix M 173 | The function `influence()` will return an array of influences for each user and the matrix ![$M = m_{ij}$](http://latex.codecogs.com/gif.latex?%24M%20%3D%20m_%7Bij%7D%24), where ![$m_{ij}$](http://latex.codecogs.com/gif.latex?%24m_%7Bij%7D%24) is the influence of the ![$i^{th}$](http://latex.codecogs.com/gif.latex?%24i%5E%7Bth%7D%24) tweet of the ![$j^{th}$](http://latex.codecogs.com/gif.latex?%24j%5E%7Bth%7D%24) tweet (direct and indirect). 174 | 175 | 176 | ```python 177 | inf, m_ij = influence(p_ij) 178 | ``` 179 | 180 | ### Link influence with user_id 181 | 182 | Now, we add the computed user influence back to the pandas data structure. 183 | 184 | 185 | ```python 186 | cascade["influence"] = pd.Series(inf) 187 | cascade.head() 188 | ``` 189 | 190 | 191 | 192 | 193 |
194 | 195 | 196 | 197 | 198 | 199 | 200 | 201 | 202 | 203 | 204 | 205 | 206 | 207 | 208 | 209 | 210 | 211 | 212 | 213 | 214 | 215 | 216 | 217 | 218 | 219 | 220 | 221 | 222 | 223 | 224 | 225 | 226 | 227 | 228 | 229 | 230 | 231 | 232 | 233 | 234 | 235 | 236 | 237 | 238 | 239 | 240 | 241 |
timemagnitudeuser_idinfluence
0099141960.000000
1127135265834.590370
22149205726429.656122
324651155101613.535845
42485191779015.913873
242 |
243 | 244 | 245 | 246 | ## Compute influence over multiple cascades 247 | ### Load function 248 | The function *casIn()* compute influence in one cascade, which basically contain all the steps described above 249 | 250 | 251 | ```python 252 | from casIn.user_influence import casIn 253 | influence = casIn(cascade_path="../data/SMH/SMH-cascade-0.csv",time_decay=-0.000068) 254 | influence.head() 255 | ``` 256 | 257 | 258 | 259 | 260 |
261 | 262 | 263 | 264 | 265 | 266 | 267 | 268 | 269 | 270 | 271 | 272 | 273 | 274 | 275 | 276 | 277 | 278 | 279 | 280 | 281 | 282 | 283 | 284 | 285 | 286 | 287 | 288 | 289 | 290 | 291 | 292 | 293 | 294 | 295 | 296 | 297 | 298 | 299 | 300 | 301 | 302 | 303 | 304 | 305 | 306 | 307 | 308 |
timemagnitudeuser_idinfluence
0099141960.000000
1127135265834.590370
22149205726429.656122
324651155101613.535845
42485191779015.913873
309 |
310 | 311 | 312 | 313 | ### Load multiple cascades 314 | 315 | The SMH toy dataset contains 20 cascades for testing out Cas.In. 316 | Let's load all of them: 317 | 318 | 319 | ```python 320 | cascades = [] 321 | for i in range(20): 322 | inf = casIn(cascade_path="../data/SMH/SMH-cascade-%d.csv" % i,time_decay=-0.000068) 323 | cascades.append(inf) 324 | cascades = pd.concat(cascades) 325 | ``` 326 | 327 | ### Compute user influence in multiple cascades 328 | 329 | The influence of a user is by definition the mean influence of the tweets they emit. 330 | We compute the user influence as follows: 331 | 332 | 333 | ```python 334 | result = cascades.groupby("user_id").agg({"influence" : "mean"}) 335 | result.sort_values("influence",ascending=False).head() 336 | ``` 337 | 338 | 339 | 340 | 341 |
342 | 343 | 344 | 345 | 346 | 347 | 348 | 349 | 350 | 351 | 352 | 353 | 354 | 355 | 356 | 357 | 358 | 359 | 360 | 361 | 362 | 363 | 364 | 365 | 366 | 367 | 368 | 369 | 370 | 371 | 372 | 373 | 374 | 375 |
influence
user_id
734214.000000
1225205.000000
755190.554571
60189.557461
581141.033129
376 |
377 | 378 | 379 | -------------------------------------------------------------------------------- /Tutorial.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "markdown", 5 | "metadata": {}, 6 | "source": [ 7 | "# Cascade Influence" 8 | ] 9 | }, 10 | { 11 | "cell_type": "markdown", 12 | "metadata": {}, 13 | "source": [ 14 | "This repository contains:\n", 15 | " - The scripts to estimate user influence from Twitter information cascades (i.e. Cas.In);\n", 16 | " - A small dataset of 20 cascades for testing Cas.In;\n", 17 | " - A hands-on tutorial to walk you through running Cas.In on real cascades." 18 | ] 19 | }, 20 | { 21 | "cell_type": "markdown", 22 | "metadata": {}, 23 | "source": [ 24 | "### Citation\n", 25 | "The algorithm was introduced in the paper:\n", 26 | "\n", 27 | "Rizoiu, M.-A., Graham, T., Zhang, R., Zhang, Y., Ackland, R., & Xie, L. (2018). **#DebateNight: The Role and Influence of Socialbots on Twitter During the 1st 2016 U.S. Presidential Debate**. In *Proc. International AAAI Conference on Web and Social Media (ICWSM ’18)* (pp. 1–10). Stanford, CA, USA. \n", 28 | "[pdf at arxiv with supplementary material](https://arxiv.org/abs/1802.09808)\n", 29 | "\n", 30 | "**Bibtex**\n", 31 | "```\n", 32 | "@inproceedings{rizoiu2018debatenight,\n", 33 | " address = {Stanford, CA, USA},\n", 34 | " author = {Rizoiu, Marian-Andrei and Graham, Timothy and Zhang, Rui and Zhang, Yifei and Ackland, Robert and Xie, Lexing},\n", 35 | " booktitle = {International AAAI Conference on Web and Social Media (ICWSM '18)},\n", 36 | " title = {{{\\#}DebateNight: The Role and Influence of Socialbots on Twitter During the 1st 2016 U.S. Presidential Debate}},\n", 37 | " url = {https://arxiv.org/abs/1802.09808},\n", 38 | " year = {2018}\n", 39 | "}\n", 40 | "```" 41 | ] 42 | }, 43 | { 44 | "cell_type": "markdown", 45 | "metadata": {}, 46 | "source": [ 47 | "### License\n", 48 | "Both dataset and code are distributed under the Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) license, a copy of which can be obtained following this link. If you require a different license, please contact [Yifei Zhang](mailto:yifeiacc@gmail.com), [Marian-Andrei Rizoiu](mailto:Marian-Andrei@rizoiu.eu) or [Lexing Xie](mailto:Lexing.Xie@anu.edu.au)." 49 | ] 50 | }, 51 | { 52 | "cell_type": "markdown", 53 | "metadata": {}, 54 | "source": [ 55 | "# How to run Cas.In in a terminal:\n", 56 | "\n", 57 | "### Required packages:\n", 58 | " - python3\n", 59 | " - numpy\n", 60 | " - pandas\n", 61 | " \n", 62 | "### Arguments of Cas.In:\n", 63 | "\n", 64 | "*--cascade_path* : the path of cascade file (see the format here below). \n", 65 | "\n", 66 | "*--time_decay* : the coefficient value of time decay (hyperparameter $r$ in the paper). **Default**:-0.000068\n", 67 | "\n", 68 | "*--save2csv* : save result to csv file. **Default**: False\n", 69 | "\n", 70 | "### Command:\n", 71 | "```bash\n", 72 | "cd scripts\n", 73 | "python3 influence.py --cascade_path path/to/file\n", 74 | "```" 75 | ] 76 | }, 77 | { 78 | "cell_type": "markdown", 79 | "metadata": {}, 80 | "source": [ 81 | "# File format and toy dataset\n", 82 | "\n", 83 | "### Dataset\n", 84 | "We provide a toy dataset -- dubbed SMH -- for testing Cas.In.\n", 85 | "It was collected in 2017 by following the Twitter handle of the Sydney Morning Herald newspaper (tweets and retweets mentioning SMH or linking to an article from SMH).\n", 86 | "The data contains 20 cascades (one file per cascade).\n", 87 | "We annonymized the `user_id` (as per Twitter's ToS) by mapping original values to a sequence from 0 to n, while preserving the identity of users across cascades.\n", 88 | "\n", 89 | "### The format cascade files:\n", 90 | " - A csv file with 3 columns (`time`, `magnitude`, `user_id`), where each row is a tweet in the cascade:\n", 91 | " - `time` represents the timestamp of tweet -- the first tweet is always at time zero, for the following retweets it shows the offset in seconds from the initial tweet;\n", 92 | " - `magnitude` is the local influence of the user (here the number of followers);\n", 93 | " - `user_id` the id of the user emitting the tweet (here annonymized).\n", 94 | " - The rows in the file (i.e. the tweets) are sorted by the timestamp;\n", 95 | " \n", 96 | "eg:\n", 97 | "```\n", 98 | "time,magnitude,user_id \n", 99 | "0,4674,\"0\"\n", 100 | "321,1327,\"1\"\n", 101 | "339,976,\"2\"\n", 102 | "383,477,\"3\"\n", 103 | "699,1209,\"4\"\n", 104 | "824,119,\"5\"\n", 105 | "835,1408,\"6\"\n", 106 | "1049,896,\"7\"\n", 107 | "```" 108 | ] 109 | }, 110 | { 111 | "cell_type": "markdown", 112 | "metadata": {}, 113 | "source": [ 114 | "# Cascade influence tutorial\n", 115 | "\n", 116 | "Next, we drive you through using Cas.In for estimating user influence starting from a single cascade.\n", 117 | "\n", 118 | "### Preliminary\n", 119 | "We need to first load all required packages of cascade influence." 120 | ] 121 | }, 122 | { 123 | "cell_type": "code", 124 | "execution_count": 1, 125 | "metadata": { 126 | "collapsed": false 127 | }, 128 | "outputs": [ 129 | { 130 | "name": "stdout", 131 | "output_type": "stream", 132 | "text": [ 133 | "/Users/yifei/Desktop/cascade-influence/scripts\n" 134 | ] 135 | } 136 | ], 137 | "source": [ 138 | "cd scripts" 139 | ] 140 | }, 141 | { 142 | "cell_type": "code", 143 | "execution_count": 2, 144 | "metadata": { 145 | "collapsed": true 146 | }, 147 | "outputs": [], 148 | "source": [ 149 | "import pandas as pd\n", 150 | "import numpy as np\n", 151 | "from casIn.user_influence import P,influence" 152 | ] 153 | }, 154 | { 155 | "cell_type": "markdown", 156 | "metadata": {}, 157 | "source": [ 158 | "## Compute influence in one cascade" 159 | ] 160 | }, 161 | { 162 | "cell_type": "markdown", 163 | "metadata": { 164 | "collapsed": true 165 | }, 166 | "source": [ 167 | "### Read data\n", 168 | "Load the first cascade in the SMH toy dataset:" 169 | ] 170 | }, 171 | { 172 | "cell_type": "code", 173 | "execution_count": 3, 174 | "metadata": { 175 | "collapsed": false 176 | }, 177 | "outputs": [ 178 | { 179 | "data": { 180 | "text/html": [ 181 | "
\n", 182 | "\n", 195 | "\n", 196 | " \n", 197 | " \n", 198 | " \n", 199 | " \n", 200 | " \n", 201 | " \n", 202 | " \n", 203 | " \n", 204 | " \n", 205 | " \n", 206 | " \n", 207 | " \n", 208 | " \n", 209 | " \n", 210 | " \n", 211 | " \n", 212 | " \n", 213 | " \n", 214 | " \n", 215 | " \n", 216 | " \n", 217 | " \n", 218 | " \n", 219 | " \n", 220 | " \n", 221 | " \n", 222 | " \n", 223 | " \n", 224 | " \n", 225 | " \n", 226 | " \n", 227 | " \n", 228 | " \n", 229 | " \n", 230 | " \n", 231 | " \n", 232 | " \n", 233 | " \n", 234 | " \n", 235 | " \n", 236 | "
timemagnitudeuser_id
00991419
11271352658
221492057264
3246511551016
424851917790
\n", 237 | "
" 238 | ], 239 | "text/plain": [ 240 | " time magnitude user_id\n", 241 | "0 0 991 419\n", 242 | "1 127 1352 658\n", 243 | "2 2149 2057 264\n", 244 | "3 2465 1155 1016\n", 245 | "4 2485 1917 790" 246 | ] 247 | }, 248 | "execution_count": 3, 249 | "metadata": {}, 250 | "output_type": "execute_result" 251 | } 252 | ], 253 | "source": [ 254 | "cascade = pd.read_csv(\"../data/SMH/SMH-cascade-0.csv\")\n", 255 | "cascade.head()" 256 | ] 257 | }, 258 | { 259 | "cell_type": "markdown", 260 | "metadata": {}, 261 | "source": [ 262 | "### Compute matrix P\n", 263 | "\n", 264 | "We first need to compute the probabilities $p_{ij}$, where $p_{ij}$ is the probability that $j^{th}$ tweet is a direct retweet of the $i^{th}$ (see the paper for more details).\n", 265 | "We need to specify the hyper-parameter $r$, the time decay coefficient. \n", 266 | "Here we choose $r = -0.000068$." 267 | ] 268 | }, 269 | { 270 | "cell_type": "code", 271 | "execution_count": 4, 272 | "metadata": { 273 | "collapsed": true 274 | }, 275 | "outputs": [], 276 | "source": [ 277 | "p_ij = P(cascade,r = -0.000068)" 278 | ] 279 | }, 280 | { 281 | "cell_type": "markdown", 282 | "metadata": {}, 283 | "source": [ 284 | "### Compute user influence and matrix M\n", 285 | "The function `influence()` will return an array of influences for each user and the matrix $M = m_{ij}$, where $m_{ij}$ is the influence of the $i^{th}$ tweet of the $j^{th}$ tweet (direct and indirect)." 286 | ] 287 | }, 288 | { 289 | "cell_type": "code", 290 | "execution_count": 5, 291 | "metadata": { 292 | "collapsed": true 293 | }, 294 | "outputs": [], 295 | "source": [ 296 | "inf, m_ij = influence(p_ij)" 297 | ] 298 | }, 299 | { 300 | "cell_type": "markdown", 301 | "metadata": {}, 302 | "source": [ 303 | "### Link influence with user_id\n", 304 | "\n", 305 | "Now, we add the computed user influence back to the pandas data structure." 306 | ] 307 | }, 308 | { 309 | "cell_type": "code", 310 | "execution_count": 6, 311 | "metadata": { 312 | "collapsed": true 313 | }, 314 | "outputs": [], 315 | "source": [ 316 | "cascade[\"influence\"] = pd.Series(inf)" 317 | ] 318 | }, 319 | { 320 | "cell_type": "code", 321 | "execution_count": 7, 322 | "metadata": { 323 | "collapsed": false 324 | }, 325 | "outputs": [ 326 | { 327 | "data": { 328 | "text/html": [ 329 | "
\n", 330 | "\n", 343 | "\n", 344 | " \n", 345 | " \n", 346 | " \n", 347 | " \n", 348 | " \n", 349 | " \n", 350 | " \n", 351 | " \n", 352 | " \n", 353 | " \n", 354 | " \n", 355 | " \n", 356 | " \n", 357 | " \n", 358 | " \n", 359 | " \n", 360 | " \n", 361 | " \n", 362 | " \n", 363 | " \n", 364 | " \n", 365 | " \n", 366 | " \n", 367 | " \n", 368 | " \n", 369 | " \n", 370 | " \n", 371 | " \n", 372 | " \n", 373 | " \n", 374 | " \n", 375 | " \n", 376 | " \n", 377 | " \n", 378 | " \n", 379 | " \n", 380 | " \n", 381 | " \n", 382 | " \n", 383 | " \n", 384 | " \n", 385 | " \n", 386 | " \n", 387 | " \n", 388 | " \n", 389 | " \n", 390 | "
timemagnitudeuser_idinfluence
0099141960.000000
1127135265834.590370
22149205726429.656122
324651155101613.535845
42485191779015.913873
\n", 391 | "
" 392 | ], 393 | "text/plain": [ 394 | " time magnitude user_id influence\n", 395 | "0 0 991 419 60.000000\n", 396 | "1 127 1352 658 34.590370\n", 397 | "2 2149 2057 264 29.656122\n", 398 | "3 2465 1155 1016 13.535845\n", 399 | "4 2485 1917 790 15.913873" 400 | ] 401 | }, 402 | "execution_count": 7, 403 | "metadata": {}, 404 | "output_type": "execute_result" 405 | } 406 | ], 407 | "source": [ 408 | "cascade.head()" 409 | ] 410 | }, 411 | { 412 | "cell_type": "markdown", 413 | "metadata": {}, 414 | "source": [ 415 | "## Compute influence over multiple cascades\n", 416 | "### Load function\n", 417 | "The function *casIn()* compute influence in one cascade, which basically contain all the steps described above" 418 | ] 419 | }, 420 | { 421 | "cell_type": "code", 422 | "execution_count": 8, 423 | "metadata": { 424 | "collapsed": true 425 | }, 426 | "outputs": [], 427 | "source": [ 428 | "from casIn.user_influence import casIn" 429 | ] 430 | }, 431 | { 432 | "cell_type": "code", 433 | "execution_count": 9, 434 | "metadata": { 435 | "collapsed": false 436 | }, 437 | "outputs": [ 438 | { 439 | "data": { 440 | "text/html": [ 441 | "
\n", 442 | "\n", 455 | "\n", 456 | " \n", 457 | " \n", 458 | " \n", 459 | " \n", 460 | " \n", 461 | " \n", 462 | " \n", 463 | " \n", 464 | " \n", 465 | " \n", 466 | " \n", 467 | " \n", 468 | " \n", 469 | " \n", 470 | " \n", 471 | " \n", 472 | " \n", 473 | " \n", 474 | " \n", 475 | " \n", 476 | " \n", 477 | " \n", 478 | " \n", 479 | " \n", 480 | " \n", 481 | " \n", 482 | " \n", 483 | " \n", 484 | " \n", 485 | " \n", 486 | " \n", 487 | " \n", 488 | " \n", 489 | " \n", 490 | " \n", 491 | " \n", 492 | " \n", 493 | " \n", 494 | " \n", 495 | " \n", 496 | " \n", 497 | " \n", 498 | " \n", 499 | " \n", 500 | " \n", 501 | " \n", 502 | "
timemagnitudeuser_idinfluence
0099141960.000000
1127135265834.590370
22149205726429.656122
324651155101613.535845
42485191779015.913873
\n", 503 | "
" 504 | ], 505 | "text/plain": [ 506 | " time magnitude user_id influence\n", 507 | "0 0 991 419 60.000000\n", 508 | "1 127 1352 658 34.590370\n", 509 | "2 2149 2057 264 29.656122\n", 510 | "3 2465 1155 1016 13.535845\n", 511 | "4 2485 1917 790 15.913873" 512 | ] 513 | }, 514 | "execution_count": 9, 515 | "metadata": {}, 516 | "output_type": "execute_result" 517 | } 518 | ], 519 | "source": [ 520 | "influence = casIn(cascade_path=\"../data/SMH/SMH-cascade-0.csv\",time_decay=-0.000068)\n", 521 | "influence.head()" 522 | ] 523 | }, 524 | { 525 | "cell_type": "markdown", 526 | "metadata": {}, 527 | "source": [ 528 | "### Load multiple cascades\n", 529 | "\n", 530 | "The SMH toy dataset contains 20 cascades for testing out Cas.In.\n", 531 | "Let's load all of them:" 532 | ] 533 | }, 534 | { 535 | "cell_type": "code", 536 | "execution_count": 10, 537 | "metadata": { 538 | "collapsed": true 539 | }, 540 | "outputs": [], 541 | "source": [ 542 | "cascades = []\n", 543 | "for i in range(20):\n", 544 | " inf = casIn(cascade_path=\"../data/SMH/SMH-cascade-%d.csv\" % i,time_decay=-0.000068)\n", 545 | " cascades.append(inf)\n", 546 | "cascades = pd.concat(cascades)" 547 | ] 548 | }, 549 | { 550 | "cell_type": "markdown", 551 | "metadata": {}, 552 | "source": [ 553 | "### Compute user influence in multiple cascades\n", 554 | "\n", 555 | "The influence of a user is by definition the mean influence of the tweets they emit.\n", 556 | "We compute the user influence as follows:" 557 | ] 558 | }, 559 | { 560 | "cell_type": "code", 561 | "execution_count": 11, 562 | "metadata": { 563 | "collapsed": true 564 | }, 565 | "outputs": [], 566 | "source": [ 567 | "result = cascades.groupby(\"user_id\").agg({\"influence\" : \"mean\"})" 568 | ] 569 | }, 570 | { 571 | "cell_type": "code", 572 | "execution_count": 12, 573 | "metadata": { 574 | "collapsed": false 575 | }, 576 | "outputs": [ 577 | { 578 | "data": { 579 | "text/html": [ 580 | "
\n", 581 | "\n", 594 | "\n", 595 | " \n", 596 | " \n", 597 | " \n", 598 | " \n", 599 | " \n", 600 | " \n", 601 | " \n", 602 | " \n", 603 | " \n", 604 | " \n", 605 | " \n", 606 | " \n", 607 | " \n", 608 | " \n", 609 | " \n", 610 | " \n", 611 | " \n", 612 | " \n", 613 | " \n", 614 | " \n", 615 | " \n", 616 | " \n", 617 | " \n", 618 | " \n", 619 | " \n", 620 | " \n", 621 | " \n", 622 | " \n", 623 | " \n", 624 | " \n", 625 | " \n", 626 | " \n", 627 | "
influence
user_id
734214.000000
1225205.000000
755190.554571
60189.557461
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" 629 | ], 630 | "text/plain": [ 631 | " influence\n", 632 | "user_id \n", 633 | "734 214.000000\n", 634 | "1225 205.000000\n", 635 | "755 190.554571\n", 636 | "60 189.557461\n", 637 | "581 141.033129" 638 | ] 639 | }, 640 | "execution_count": 12, 641 | "metadata": {}, 642 | "output_type": "execute_result" 643 | } 644 | ], 645 | "source": [ 646 | "result.sort_values(\"influence\",ascending=False).head()" 647 | ] 648 | }, 649 | { 650 | "cell_type": "code", 651 | "execution_count": null, 652 | "metadata": { 653 | "collapsed": true 654 | }, 655 | "outputs": [], 656 | "source": [] 657 | } 658 | ], 659 | "metadata": { 660 | "kernelspec": { 661 | "display_name": "Python [Root]", 662 | "language": "python", 663 | "name": "Python [Root]" 664 | }, 665 | "language_info": { 666 | "codemirror_mode": { 667 | "name": "ipython", 668 | "version": 2 669 | }, 670 | "file_extension": ".py", 671 | "mimetype": "text/x-python", 672 | "name": "python", 673 | "nbconvert_exporter": "python", 674 | "pygments_lexer": "ipython2", 675 | "version": "2.7.11" 676 | } 677 | }, 678 | "nbformat": 4, 679 | "nbformat_minor": 2 680 | } 681 | -------------------------------------------------------------------------------- /data/SMH/SMH-cascade-0.csv: -------------------------------------------------------------------------------- 1 | time,magnitude,user_id 2 | 0,991,419 3 | 127,1352,658 4 | 2149,2057,264 5 | 2465,1155,1016 6 | 2485,1917,790 7 | 2604,1484,942 8 | 2781,1474,1006 9 | 2996,1443,1064 10 | 3344,2197,491 11 | 4142,2220,1083 12 | 4208,5223,532 13 | 5004,1387,691 14 | 5324,1387,691 15 | 5526,1899,1140 16 | 5543,486,536 17 | 5626,676,559 18 | 6099,619,870 19 | 6969,620,766 20 | 8361,628,1631 21 | 9124,16912,1180 22 | 9290,411,473 23 | 9327,706,596 24 | 9525,450,800 25 | 14821,360,1441 26 | 16066,1298,371 27 | 20625,5042,932 28 | 20984,1369,8 29 | 21224,2803,465 30 | 22575,1555,285 31 | 24405,3469,1173 32 | 24442,696,1653 33 | 25705,835,1414 34 | 28484,973,307 35 | 42711,1406,471 36 | 43759,475,1424 37 | 43788,98,1516 38 | 48561,1417,1343 39 | 48641,1889,1265 40 | 48660,736,1091 41 | 48859,2238,1297 42 | 50520,4006,373 43 | 50529,2535,866 44 | 50938,506,1157 45 | 51376,1689,1107 46 | 51769,104,455 47 | 51954,2372,1065 48 | 51994,295,593 49 | 52353,1946,252 50 | 52409,443,44 51 | 52737,98,1152 52 | 53188,469,1660 53 | 53209,1051,1381 54 | 53858,3781,200 55 | 53962,276,545 56 | 58433,5,1635 57 | 64675,1881,549 58 | 86711,568,696 59 | 102008,3274,1324 60 | 152386,3196,1312 61 | 162980,407,1313 62 | -------------------------------------------------------------------------------- /data/SMH/SMH-cascade-1.csv: -------------------------------------------------------------------------------- 1 | time,magnitude,user_id 2 | 0,45907,81 3 | 95,12347,1547 4 | 375,1,1596 5 | 385,1,1593 6 | 390,1,1684 7 | 394,10,1617 8 | 394,1,1592 9 | 394,2,1590 10 | 395,1,1597 11 | 404,1,1591 12 | 404,1,1627 13 | 405,1,1589 14 | 406,1,1595 15 | 412,1,1588 16 | 418,1,1583 17 | 423,1,1604 18 | 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-------------------------------------------------------------------------------- /data/SMH/SMH-cascade-11.csv: -------------------------------------------------------------------------------- 1 | time,magnitude,user_id 2 | 0,22486,215 3 | 215,6658,996 4 | 233,1068,890 5 | 357,65,1020 6 | 380,4016,161 7 | 482,393,1275 8 | 505,1598,808 9 | 1006,248,241 10 | 1271,1566,840 11 | 1772,753,786 12 | 2022,396,1300 13 | 2083,720,747 14 | 2162,151,1240 15 | 2490,521,1341 16 | 2831,480,1101 17 | 3339,306,148 18 | 3385,176,847 19 | 4298,1690,1248 20 | 4355,1565,564 21 | 4539,83,1512 22 | 5527,2382,578 23 | 5558,606,878 24 | 6582,3250,1118 25 | 6697,2014,924 26 | 7461,1403,974 27 | 7688,355,41 28 | 8930,282,835 29 | 14297,2580,18 30 | 15916,548,696 31 | 16322,251,442 32 | 17033,2130,1297 33 | 20204,120107,375 34 | 20249,744,1303 35 | 20305,740,434 36 | 20425,109,1326 37 | 20459,576,171 38 | 20625,602,680 39 | 21642,1052,503 40 | 25383,7409,494 41 | 25522,35,688 42 | 26814,537,759 43 | 27241,3689,432 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127026,1927,1060 115 | 127120,2239,170 116 | 127324,2548,547 117 | 127720,1186,1212 118 | 132356,1141,585 119 | 140551,771,592 120 | 148192,1809,814 121 | 160465,442,728 122 | 298884,2363,485 123 | 299114,828,1434 124 | 300710,174,742 125 | 300805,1429,62 126 | 303201,3155,1378 127 | 303239,8847,811 128 | 303282,1679,935 129 | 304299,173,560 130 | 307138,1447,98 131 | 307406,1177,654 132 | 324190,478,1424 133 | -------------------------------------------------------------------------------- /data/SMH/SMH-cascade-15.csv: -------------------------------------------------------------------------------- 1 | time,magnitude,user_id 2 | 0,678,1612 3 | 1220,117,117 4 | 3871,576,1124 5 | 4868,18109,755 6 | 4903,374,109 7 | 4903,11757,118 8 | 4911,5630,120 9 | 4981,1642,975 10 | 5095,1629,1214 11 | 5554,1924,484 12 | 5674,7896,1485 13 | 5697,8665,238 14 | 5776,2957,1089 15 | 5797,626,111 16 | 5820,3390,5 17 | 5908,4116,458 18 | 6188,880,493 19 | 6253,820,766 20 | 6790,1701,1264 21 | 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72 | 578431,137,1640 73 | -------------------------------------------------------------------------------- /data/SMH/SMH-cascade-16.csv: -------------------------------------------------------------------------------- 1 | time,magnitude,user_id 2 | 0,2733,880 3 | 89,1368,163 4 | 1821,1817,466 5 | 1992,2204,1280 6 | 2271,2282,1297 7 | 2642,2381,912 8 | 3311,198,1665 9 | 4601,3841,567 10 | 4822,451,660 11 | 6845,1921,1041 12 | 7312,14901,1074 13 | 7478,1074,1185 14 | 8052,1677,323 15 | 8186,354,1463 16 | 8229,750,3 17 | 8467,927,766 18 | 9036,11976,302 19 | 9058,1148,761 20 | 9396,2302,998 21 | 9571,1750,1224 22 | 9804,1676,366 23 | 10332,2849,1553 24 | 10859,2830,1061 25 | 12756,711,1179 26 | 15521,2632,75 27 | 15703,1285,550 28 | 16403,2599,1634 29 | 17213,370,767 30 | 17778,2720,18 31 | 17871,3685,1150 32 | 20140,3642,1173 33 | 20670,3209,1540 34 | 21323,1765,836 35 | 23584,806,1104 36 | 27308,314,586 37 | 30104,2521,1257 38 | 30682,4235,1447 39 | 30729,1487,823 40 | 32252,349,1238 41 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-------------------------------------------------------------------------------- /data/SMH/SMH-cascade-18.csv: -------------------------------------------------------------------------------- 1 | time,magnitude,user_id 2 | 0,5020,1225 3 | 16,1274,368 4 | 95,363,1350 5 | 98,1499,1467 6 | 148,5029,538 7 | 172,134,868 8 | 306,696,807 9 | 343,401,1423 10 | 502,1432,486 11 | 510,2529,547 12 | 586,220,881 13 | 595,1436,802 14 | 749,742,175 15 | 812,2305,485 16 | 851,157,1413 17 | 853,4569,90 18 | 972,2381,574 19 | 1111,1212,546 20 | 1161,1686,266 21 | 1166,1854,1187 22 | 1247,110,953 23 | 1890,983,1336 24 | 1911,8745,811 25 | 1958,5237,632 26 | 1983,3997,370 27 | 1993,950,1193 28 | 2033,4934,631 29 | 2123,185,1669 30 | 2133,3854,594 31 | 2161,4778,99 32 | 2187,577,152 33 | 2290,1212,198 34 | 2346,956,325 35 | 2347,438,516 36 | 2503,107,1640 37 | 2536,1542,406 38 | 2840,1672,88 39 | 2854,960,56 40 | 3411,624,1466 41 | 3449,938,662 42 | 3771,919,1526 43 | 4157,3728,151 44 | 4504,2077,1142 45 | 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time,magnitude,user_id 2 | 0,8715,1427 3 | 29,35,1479 4 | 104,178,1298 5 | 133,43,1437 6 | 226,69,1482 7 | 323,31,1451 8 | 420,22,1217 9 | 511,34,1491 10 | 601,44,1487 11 | 690,39,1532 12 | 783,23,1231 13 | 884,17,1198 14 | 1051,29,1454 15 | 1143,71,1439 16 | 1227,14,1352 17 | 1308,42,1534 18 | 1567,26,1254 19 | 1657,12,1291 20 | 1751,72,1442 21 | 1882,22,1452 22 | 1929,21,1197 23 | 2025,43,1489 24 | 2105,31,1245 25 | 2193,52,1446 26 | 2277,56,1440 27 | 2390,13,1344 28 | 2447,29,1272 29 | 2616,247,1239 30 | 2699,27,1223 31 | 2794,15,1293 32 | 2874,25,1542 33 | 2958,91,1462 34 | 3035,17,1234 35 | 3121,18,1237 36 | 3207,58,1438 37 | 3463,34,1287 38 | 3553,25,1316 39 | 3648,46,1453 40 | 3720,33,1182 41 | 3802,29,1243 42 | 3885,156,1443 43 | 3973,37,1533 44 | 4054,130,1256 45 | 4142,38,1513 46 | 4222,32,1478 47 | 4314,20,1334 48 | 4389,39,1227 49 | 4476,26,1233 50 | 4548,28,1278 51 | 4633,45,1539 52 | 79480,612,1139 53 | -------------------------------------------------------------------------------- /data/SMH/SMH-cascade-3.csv: -------------------------------------------------------------------------------- 1 | time,magnitude,user_id 2 | 0,6936,384 3 | 62,1673,664 4 | 84,883,1202 5 | 95,4195,258 6 | 97,1996,313 7 | 108,3937,541 8 | 159,6994,321 9 | 180,12143,232 10 | 186,583,428 11 | 256,2709,496 12 | 259,362,863 13 | 367,3103,165 14 | 433,3493,1205 15 | 488,17445,211 16 | 1324,88,91 17 | 1512,2282,799 18 | 3991,294,1112 19 | 5181,855,276 20 | 6222,16694,677 21 | 6242,82,778 22 | 6256,209,618 23 | 8357,7032,328 24 | 8370,2991,634 25 | 8396,4736,1178 26 | 8478,13,1663 27 | 8907,3735,27 28 | 8910,3432,93 29 | 9175,13149,17 30 | 9714,92,424 31 | 10524,5380,97 32 | 11272,194,300 33 | 11919,236,827 34 | 12868,2343,278 35 | 13230,138,1050 36 | 13689,26,289 37 | 14201,1071,497 38 | 14499,448,756 39 | 15020,53798,227 40 | 15089,3695,179 41 | 15137,344,1307 42 | 15182,4485,849 43 | 15199,131,877 44 | 15209,1098,587 45 | 15257,1237,672 46 | 15268,804,51 47 | 15305,424,248 48 | 15356,228,1003 49 | 15370,870,600 50 | 15715,5594,235 51 | 15979,109,25 52 | 16177,1239,163 53 | 16255,821,795 54 | 17977,2183,1068 55 | 18076,254,925 56 | 18477,1837,407 57 | 18681,429,435 58 | 19412,783,659 59 | 19446,202,1578 60 | 19873,165,746 61 | 23243,480,1345 62 | 23744,622,1261 63 | 29980,1279,733 64 | 30818,3830,287 65 | 30944,1605,322 66 | 31566,644,403 67 | 32180,143,192 68 | 32594,234,1038 69 | 32903,4884,584 70 | 33743,955,7 71 | 36573,481,177 72 | 39991,477,943 73 | 40152,1012,1269 74 | 77596,112,1428 75 | 87115,257,1102 76 | 89787,1690,356 77 | 161809,3364,1273 78 | -------------------------------------------------------------------------------- /data/SMH/SMH-cascade-4.csv: -------------------------------------------------------------------------------- 1 | time,magnitude,user_id 2 | 0,42464,675 3 | 18,1008,280 4 | 38,34,1348 5 | 68,2333,448 6 | 320,196,1667 7 | 338,48,1458 8 | 356,202,364 9 | 386,346,1557 10 | 514,94,28 11 | 578,2973,92 12 | 819,1279,1226 13 | 1073,7439,182 14 | 1123,661,1125 15 | 1276,136,1502 16 | 1334,1429,1315 17 | 2082,143,859 18 | 2524,349,291 19 | 3195,81,332 20 | 3285,27,777 21 | 3769,12,884 22 | 4591,314,897 23 | 4710,8745,1369 24 | 5898,31,1418 25 | 6343,2051,133 26 | 6795,1201,1382 27 | 7925,26,379 28 | 8599,115,936 29 | 10043,159,1274 30 | 11001,76,1020 31 | 12551,19,771 32 | 13167,231,347 33 | 13590,1005,306 34 | 14610,106,765 35 | 14884,37,1497 36 | 15098,361,1460 37 | 15475,6,1657 38 | 17443,19,689 39 | 17475,4215,184 40 | 17537,1641,579 41 | 18959,4014,683 42 | 19336,48,1455 43 | 22328,32,1643 44 | 24845,2407,1521 45 | 26336,14,1518 46 | 30939,66,1546 47 | 35341,38,400 48 | 40482,1317,556 49 | 44759,262,183 50 | 44781,39,341 51 | 49452,53,954 52 | 49478,1144,563 53 | 53617,11,1059 54 | 53718,158,1456 55 | -------------------------------------------------------------------------------- /data/SMH/SMH-cascade-5.csv: -------------------------------------------------------------------------------- 1 | time,magnitude,user_id 2 | 0,23008,215 3 | 39,1730,720 4 | 205,677,329 5 | 276,2103,1156 6 | 321,293,1268 7 | 373,9136,286 8 | 397,261,150 9 | 520,1875,86 10 | 950,14848,1074 11 | 991,1464,1343 12 | 1010,783,592 13 | 1384,4769,1206 14 | 1590,53,1496 15 | 1859,584,1149 16 | 1924,4221,344 17 | 2016,461,1621 18 | 2665,1539,963 19 | 3616,371,1331 20 | 3918,543,1275 21 | 4537,1592,564 22 | 4719,348,213 23 | 4982,5741,1561 24 | 5557,1737,1134 25 | 7496,12159,50 26 | 8478,325,447 27 | 8558,51064,43 28 | 8596,18349,190 29 | 8608,1336,163 30 | 8616,2098,838 31 | 8633,244,347 32 | 8706,723,11 33 | 8829,2749,52 34 | 9024,1059,67 35 | 9130,184,340 36 | 11098,299,1463 37 | 11140,903,704 38 | 11319,2629,102 39 | 11868,70,591 40 | 12027,885,454 41 | 13136,295,256 42 | 14302,403,1347 43 | 15296,115,1512 44 | 15973,1725,705 45 | 16443,317,830 46 | 20548,741,390 47 | 28202,23014,215 48 | 28289,7638,261 49 | 28412,448,597 50 | 28416,559,665 51 | 28745,1396,740 52 | 30358,2256,1297 53 | 30379,1152,222 54 | 30582,1627,1214 55 | 30700,5,1687 56 | 30763,42,1063 57 | 30974,314,583 58 | 31004,305,196 59 | 31352,103,1321 60 | 32700,1900,229 61 | 34703,214,1209 62 | 34989,1061,311 63 | 37165,448,23 64 | 37197,3365,1118 65 | 37214,4578,461 66 | 38158,1755,513 67 | 40783,101,1387 68 | 41937,950,1235 69 | 70426,1205,563 70 | 70931,246,63 71 | 92008,2901,1527 72 | 95934,9478,905 73 | 97740,10928,681 74 | 106165,3164,968 75 | 106279,2296,638 76 | 111531,260,1092 77 | 112054,1268,1161 78 | 154562,1718,1267 79 | 155363,32,1633 80 | 158939,885,1511 81 | -------------------------------------------------------------------------------- /data/SMH/SMH-cascade-6.csv: -------------------------------------------------------------------------------- 1 | time,magnitude,user_id 2 | 0,2752,1061 3 | 16,6109,731 4 | 32,705,885 5 | 338,4511,1005 6 | 521,97,1290 7 | 847,1210,682 8 | 3323,505,1376 9 | 6084,4266,613 10 | 6541,1027,1282 11 | 7106,11857,302 12 | 7258,719,588 13 | 7628,1038,1043 14 | 7685,254,1616 15 | 7813,14792,1074 16 | 7854,740,901 17 | 7909,158,999 18 | 7930,1388,475 19 | 8003,4886,1129 20 | 8453,653,114 21 | 8680,893,0 22 | 9061,1357,1049 23 | 11037,166,1071 24 | 11823,705,540 25 | 12972,384,336 26 | 13117,1528,712 27 | 22056,366,1238 28 | 22548,794,1358 29 | 23122,3122,64 30 | 25883,369,1001 31 | 32520,2360,346 32 | 32691,1944,252 33 | 32810,8822,811 34 | 33302,2668,382 35 | 34162,82182,249 36 | 34339,185,1522 37 | 34622,123,1310 38 | 34700,37,1379 39 | 35872,412,427 40 | 36190,159,919 41 | 36522,713,655 42 | 36931,367,392 43 | 37561,1043,260 44 | 38485,18,1403 45 | 38591,66,1242 46 | 39455,4342,499 47 | 39655,313,736 48 | 39763,922,1110 49 | 40024,4155,1123 50 | 40692,1444,1343 51 | 41313,136,129 52 | 41708,4112,1000 53 | 42237,626,1650 54 | 42746,648,472 55 | 43921,1061,1271 56 | 45139,1760,837 57 | 45174,546,1524 58 | 45308,659,1192 59 | 45730,19449,514 60 | 45843,3241,185 61 | 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| 109939,676,828 112 | 117049,3308,911 113 | 128610,74,576 114 | 134623,3155,1378 115 | 134993,1283,1008 116 | 135761,915,381 117 | 139375,298,593 118 | 149124,178,340 119 | 150659,1404,691 120 | 168372,50,1541 121 | 228277,1614,399 122 | 229138,861,1163 123 | 248224,1308,1302 124 | 248842,1946,378 125 | 251502,1808,466 126 | 251598,1139,585 127 | -------------------------------------------------------------------------------- /data/SMH/SMH-cascade-7.csv: -------------------------------------------------------------------------------- 1 | time,magnitude,user_id 2 | 0,120735,138 3 | 70,401,453 4 | 114,1344,163 5 | 124,1989,156 6 | 128,73,275 7 | 172,2318,941 8 | 191,114,315 9 | 221,1596,351 10 | 237,11926,933 11 | 246,125,31 12 | 299,398,865 13 | 415,1526,1467 14 | 454,1508,1 15 | 472,44,167 16 | 539,99,971 17 | 545,5300,104 18 | 610,24,1174 19 | 636,31,107 20 | 650,1215,708 21 | 652,2178,135 22 | 819,354,1626 23 | 931,143,619 24 | 946,222,1474 25 | 950,3125,270 26 | 1034,289,153 27 | 1087,1823,1248 28 | 1306,816,575 29 | 1436,334,528 30 | 1568,6,1076 31 | 1607,52,1012 32 | 1704,1666,1036 33 | 1704,3890,517 34 | 1998,1673,37 35 | 2087,81,1160 36 | 2224,958,106 37 | 2412,8315,438 38 | 2412,4227,702 39 | 2507,1753,946 40 | 2595,274,208 41 | 2650,2331,1153 42 | 2717,353,846 43 | 2768,83,1484 44 | 2843,1883,842 45 | 2874,1135,69 46 | 3015,1469,394 47 | 3101,7891,195 48 | 3168,2643,267 49 | 3270,81,480 50 | 3406,4313,1169 51 | 3425,1748,1047 52 | 3538,472,220 53 | 3554,4626,602 54 | 3752,2340,784 55 | 3836,379,85 56 | 3896,143,1405 57 | 3905,1660,750 58 | 4143,961,70 59 | 4217,226,964 60 | 4266,965,408 61 | 4375,1097,402 62 | 4385,1713,995 63 | 4389,185,139 64 | 4434,188,1538 65 | 4922,55,481 66 | 5150,34,1067 67 | 5178,192,294 68 | 5353,41,207 69 | 5565,27,376 70 | 5967,703,49 71 | 6532,409,334 72 | 6659,668,236 73 | 7029,1214,357 74 | 7062,308,162 75 | 7405,97,966 76 | 7614,54,1353 77 | 7804,14,718 78 | 7815,1842,1080 79 | 7875,203,1122 80 | 7969,682,633 81 | 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43003,549,309 132 | 50208,50,355 133 | 54241,164,957 134 | 65698,2297,1520 135 | 89079,44,1673 136 | 185406,33,201 137 | 219472,493,116 138 | 264236,22021,132 139 | 389181,1374,556 140 | -------------------------------------------------------------------------------- /data/SMH/SMH-cascade-8.csv: -------------------------------------------------------------------------------- 1 | time,magnitude,user_id 2 | 0,9361,224 3 | 475,244,369 4 | 1737,536,568 5 | 3412,211,874 6 | 4078,7815,450 7 | 4481,23513,290 8 | 4692,190,1026 9 | 5049,182,1375 10 | 5393,4472,180 11 | 5902,674,729 12 | 6250,1592,397 13 | 6276,86,614 14 | 7001,106,1531 15 | 7394,1861,629 16 | 7805,838,510 17 | 8314,105,1325 18 | 9436,40,1577 19 | 10816,273,887 20 | 11503,408,159 21 | 12272,481,1337 22 | 13253,4128,202 23 | 13425,571,413 24 | 13425,4128,202 25 | 14880,294,430 26 | 16905,3505,456 27 | 17635,1396,187 28 | 18097,26,826 29 | 19248,246,930 30 | 19582,784,1415 31 | 20450,1698,1317 32 | 20876,479,642 33 | 24047,563,612 34 | 24247,4419,141 35 | 24367,80,1558 36 | 25613,106,902 37 | 26527,1154,521 38 | 26625,43,1399 39 | 29764,889,949 40 | 30544,78,601 41 | 34821,500,958 42 | 35759,74,1113 43 | 40037,39,1659 44 | 41187,545,1057 45 | 43948,329,1361 46 | 51614,262,1029 47 | 52038,2479,1088 48 | 55787,1051,1186 49 | 58420,313,1548 50 | 60883,868,437 51 | 84164,229,431 52 | 86942,1112,558 53 | 88034,458,417 54 | 89174,547,566 55 | 91051,153,1536 56 | 92642,292,676 57 | 101721,1312,1137 58 | 110287,90,1473 59 | 136096,7553,389 60 | 136254,9008,317 61 | 168656,151,1309 62 | 187527,65,1191 63 | 232690,158,1030 64 | -------------------------------------------------------------------------------- /data/SMH/SMH-cascade-9.csv: -------------------------------------------------------------------------------- 1 | time,magnitude,user_id 2 | 0,2653,604 3 | 182,725,1141 4 | 923,427,1252 5 | 1250,4729,53 6 | 5548,4439,1189 7 | 5596,2420,831 8 | 14859,3708,1262 9 | 14947,1803,814 10 | 15116,1833,1060 11 | 15290,705,205 12 | 15631,2070,928 13 | 17183,11,1672 14 | 21592,1550,561 15 | 21645,2192,491 16 | 21894,594,1355 17 | 24833,917,1266 18 | 24862,3642,393 19 | 25091,56,817 20 | 25352,10409,557 21 | 25504,3984,1070 22 | 25668,4260,805 23 | 25741,2505,1019 24 | 25743,4187,1447 25 | 25917,1398,1343 26 | 26592,995,194 27 | 26723,442,735 28 | 26994,1230,851 29 | 27224,1522,555 30 | 27277,1831,1041 31 | 27347,1236,794 32 | 27572,377,1619 33 | 28817,1379,8 34 | 28820,272,970 35 | 29620,735,506 36 | 29874,7886,354 37 | 30964,19306,514 38 | 33048,95,1289 39 | 33216,3505,743 40 | 33951,204,251 41 | 36340,1227,937 42 | 36489,59,1416 43 | 36734,124,1507 44 | 36788,3554,1150 45 | 37728,630,913 46 | 38390,284,626 47 | 38503,2251,1004 48 | 38743,294,1519 49 | 39442,217,644 50 | 41620,4595,844 51 | 42793,182,1445 52 | 47523,42,155 53 | 47968,538,508 54 | 53950,2288,530 55 | 54058,3112,1406 56 | 55730,1177,350 57 | 55791,1505,1648 58 | 55823,4193,613 59 | 58648,1440,869 60 | 61707,3071,64 61 | 67597,462,775 62 | 76315,1804,466 63 | 76849,741,810 64 | 79013,936,916 65 | 79206,165,297 66 | 84711,773,1359 67 | 85784,186,1136 68 | 85828,2259,1200 69 | 86771,2886,701 70 | 86796,2202,1195 71 | 86911,4971,1449 72 | 87093,3231,185 73 | 87210,628,1276 74 | 87657,1584,366 75 | 92116,516718,136 76 | 92188,17,1403 77 | 92243,154,876 78 | 92318,291,598 79 | 92356,151,246 80 | 92370,63,1465 81 | 93020,798,29 82 | 93617,219,894 83 | 93714,1013,343 84 | 94631,184,1333 85 | 94769,83,900 86 | 95596,2457,112 87 | 95723,3652,567 88 | 96119,1359,910 89 | 96613,129,1495 90 | 96869,1042,34 91 | 97092,4872,584 92 | 97694,3,1678 93 | 98041,1281,371 94 | 98101,3724,839 95 | 99990,25,1219 96 | 103660,696,984 97 | 103684,8733,811 98 | 104220,3735,1145 99 | 105771,17,1675 100 | 116256,314,774 101 | 125479,777,1104 102 | 158038,1174,1130 103 | 188078,117,305 104 | 561223,2783,1527 105 | 561403,2226,1119 106 | 563177,492,176 107 | 563197,1423,1343 108 | -------------------------------------------------------------------------------- /data/hashtags-political-bias.xlsx: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/computationalmedia/cascade-influence/fa437415af378f0936198c55a6d0bbc049234835/data/hashtags-political-bias.xlsx -------------------------------------------------------------------------------- /scripts/__init__.py: -------------------------------------------------------------------------------- 1 | __all__ = ["casIn"] -------------------------------------------------------------------------------- /scripts/__pycache__/__init__.cpython-36.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/computationalmedia/cascade-influence/fa437415af378f0936198c55a6d0bbc049234835/scripts/__pycache__/__init__.cpython-36.pyc -------------------------------------------------------------------------------- /scripts/__pycache__/influence.cpython-36.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/computationalmedia/cascade-influence/fa437415af378f0936198c55a6d0bbc049234835/scripts/__pycache__/influence.cpython-36.pyc -------------------------------------------------------------------------------- /scripts/__pycache__/user_influence.cpython-36.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/computationalmedia/cascade-influence/fa437415af378f0936198c55a6d0bbc049234835/scripts/__pycache__/user_influence.cpython-36.pyc -------------------------------------------------------------------------------- /scripts/casIn/__init__.py: -------------------------------------------------------------------------------- 1 | __all__ = ["user_influence"] 2 | -------------------------------------------------------------------------------- /scripts/casIn/__pycache__/__init__.cpython-36.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/computationalmedia/cascade-influence/fa437415af378f0936198c55a6d0bbc049234835/scripts/casIn/__pycache__/__init__.cpython-36.pyc -------------------------------------------------------------------------------- /scripts/casIn/__pycache__/user_influence.cpython-36.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/computationalmedia/cascade-influence/fa437415af378f0936198c55a6d0bbc049234835/scripts/casIn/__pycache__/user_influence.cpython-36.pyc -------------------------------------------------------------------------------- /scripts/casIn/user_influence.py: -------------------------------------------------------------------------------- 1 | from functools import reduce 2 | import pandas as pd 3 | import numpy as np 4 | 5 | def casIn(cascade_path, time_decay): 6 | """ 7 | compute influence in one cascade 8 | """ 9 | 10 | cascade = pd.read_csv(cascade_path) # Read one cascade from local file 11 | p_ij = P(cascade, r=time_decay) # compute p_ij in given cascade 12 | inf, m_ij = influence(p_ij) # compute user influence 13 | cascade["influence"] = pd.Series(inf) 14 | return cascade 15 | 16 | 17 | def P(cascade,r = -0.000068): 18 | """ 19 | this function compute the maritx P of a cascade 20 | """ 21 | 22 | n = len(cascade) 23 | t = np.zeros(n,dtype = np.float64) 24 | f = np.zeros(n,dtype = np.float64) 25 | p = np.zeros((n,n),dtype = np.float64) 26 | norm = np.zeros(n,dtype = np.float64) 27 | for k, row in cascade.iterrows(): 28 | if k == 0: 29 | p[0][0] = 1 30 | t[0] = row['time'] 31 | f[0] = 1 if row['magnitude'] == 0 else row['magnitude'] 32 | continue 33 | 34 | t[k] = row['time'] 35 | f[k] = 1 if row['magnitude'] == 0 else row['magnitude'] 36 | p[:k, k] = ((r * (t[k] - t[0:k])) + np.log(f[0:k])) # store the P_ji in log space 37 | norm[k] = reduce(np.logaddexp, p[:k, k]) 38 | p[:k, k] = np.exp(p[:k, k] - norm[k])# recover the P_ji from log space 39 | 40 | return p 41 | 42 | 43 | def influence(p): 44 | 45 | """Estimate user influence 46 | This function compute the user influence and store 47 | it in matirx m_ij 48 | """ 49 | n = len(p) 50 | m = np.zeros((n, n)) 51 | m[0, 0] = 1 52 | for i in range(0, n-1): 53 | vec = p[:i+1, i+1] 54 | m[:i+1, i+1] = m[:i+1, :i+1]@vec 55 | m[i+1, i+1] = 1 56 | influence = np.sum(m, axis = 1) 57 | 58 | return influence, m 59 | 60 | -------------------------------------------------------------------------------- /scripts/influence.py: -------------------------------------------------------------------------------- 1 | from casIn.user_influence import casIn 2 | import argparse 3 | 4 | parser = argparse.ArgumentParser(description='casIn') 5 | parser.add_argument('--cascade_path', type=str) 6 | parser.add_argument('--time_decay', type=float, default=-0.000068) 7 | parser.add_argument('--save2csv', type=bool, default=False) 8 | args = parser.parse_args() 9 | 10 | if __name__ == '__main__': 11 | influence = casIn(args.cascade_path, args.time_decay) 12 | print(influence) 13 | if args.save2csv: 14 | influence.to_csv("result.csv",header=True, index=False) --------------------------------------------------------------------------------