ืขืงื•ื‘ ืื—ืจ
Eduardo Graells-Garrido ๐Ÿฆ
Eduardo Graells-Garrido ๐Ÿฆ
Department of Computer Science, Universidad de Chile
ื›ืชื•ื‘ืช ืื™ืžื™ื™ืœ ืžืื•ืžืชืช ื‘ื“ื•ืžื™ื™ืŸ dcc.uchile.cl - ื“ืฃ ื”ื‘ื™ืช
ื›ื•ืชืจืช
ืฆื•ื˜ื˜ ืขืœ ื™ื“ื™
ืฆื•ื˜ื˜ ืขืœ ื™ื“ื™
ืฉื ื”
Women through the glass ceiling: gender asymmetries in Wikipediaโ€
C Wagner, E Graells-Garrido, D Garcia, F Menczerโ€
EPJ data science 5, 1-24, 2016โ€
2002016
First women, second sex: Gender bias in Wikipediaโ€
E Graells-Garrido, M Lalmas, F Menczerโ€
Proceedings of the 26th ACM conference on hypertext & social media, 165-174, 2015โ€
1542015
A city of cities: Measuring how 15-minutes urban accessibility shapes human mobility in Barcelonaโ€
E Graells-Garrido, F Serra-Burriel, F Rowe, FM Cucchietti, P Reyesโ€
PloS one 16 (5), e0250080, 2021โ€
902021
Shopping mall attraction and social mixing at a city scaleโ€
MG Beirรณ, L Bravo, D Caro, C Cattuto, L Ferres, E Graells-Garridoโ€
EPJ Data Science 7 (1), 1-21, 2018โ€
662018
Sensing urban patterns with antenna mappings: the case of Santiago, Chileโ€
E Graells-Garrido, O Peredo, J Garcรญaโ€
Sensors 16 (7), 1098, 2016โ€
662016
The effect of Pokรฉmon Go on the pulse of the city: a natural experimentโ€
E Graells-Garrido, L Ferres, D Caro, L Bravoโ€
EPJ Data Science 6, 1-19, 2017โ€
512017
Using Twitter to track immigration sentiment during early stages of the COVID-19 pandemicโ€
F Rowe, M Mahony, E Graells-Garrido, M Rango, N Sieversโ€
Data & Policy 3, e36, 2021โ€
432021
Caracterฤฑsticas de la web chilena 2004โ€
R Baeza-Yates, C Castillo, E Graellsโ€
Technical report, Center for Web Research, University of Chile, 2005โ€
422005
Visual exploration of urban dynamics using mobile dataโ€
E Graells-Garrido, J Garcรญaโ€
International Conference on Ubiquitous Computing and Ambient Intelligenceย โ€ฆ, 2015โ€
392015
Inferring modes of transportation using mobile phone dataโ€
E Graells-Garrido, D Caro, D Parraโ€
EPJ Data Science 7 (1), 1-23, 2018โ€
382018
Good times bad times: A study on recency effects in collaborative filtering for social taggingโ€
S Larrain, C Trattner, D Parra, E Graells-Garrido, K Nรธrvรฅgโ€
Proceedings of the 9th ACM Conference on Recommender Systems, 269-272, 2015โ€
382015
Data portraits: Connecting people of opposing viewsโ€
E Graells-Garrido, M Lalmas, D Querciaโ€
arXiv preprint arXiv:1311.4658, 2013โ€
352013
Data portraits and intermediary topics: Encouraging exploration of politically diverse profilesโ€
E Graells-Garrido, M Lalmas, R Baeza-Yatesโ€
Proceedings of the 21st international conference on intelligent userย โ€ฆ, 2016โ€
332016
Every colour you are: Stance prediction and turnaround in controversial issuesโ€
E Graells-Garrido, R Baeza-Yates, M Lalmasโ€
Proceedings of the 12th ACM Conference on Web Science, 174-183, 2020โ€
312020
Graphical schemes may improve readability but not understandability for people with dyslexiaโ€
L Rello, H Saggion, R Baeza-Yates, E Graellsโ€
Proceedings of the First Workshop on Predicting and Improving Textย โ€ฆ, 2012โ€
292012
Association-and perspective-based content item recommendationsโ€
D Quercia, M Lalmas, E Graellsโ€
US Patent App. 14/497,760, 2016โ€
262016
How representative is an abortion debate on Twitter?โ€
E Graells-Garrido, R Baeza-Yates, M Lalmasโ€
Proceedings of the 10th ACM Conference on Web Science, 133-134, 2019โ€
222019
People of opposing views can share common interestsโ€
E Graells-Garrido, M Lalmas, D Querciaโ€
Proceedings of the 23rd International Conference on World Wide Web, 281-282, 2014โ€
212014
Characterization of local attitudes toward immigration using social mediaโ€
Y Freire-Vidal, E Graells-Garridoโ€
Companion Proceedings of The 2019 World Wide Web Conference, 783-790, 2019โ€
202019
The WWW (and an H) of mobile application usage in the city: The what, where, when, and howโ€
E Graells-Garrido, D Caro, O Miranda, R Schifanella, OF Peredoโ€
Companion Proceedings of the The Web Conference 2018, 1221-1229, 2018โ€
202018
ื”ืžืขืจื›ืช ืื™ื ื” ื™ื›ื•ืœื” ืœื‘ืฆืข ืืช ื”ืคืขื•ืœื” ื›ืขืช. ื ืกื” ืฉื•ื‘ ืžืื•ื—ืจ ื™ื•ืชืจ.
ืžืืžืจื™ื 1–20