2017
Journal article
Open Access
Forecasting success via early adoptions analysis: a data-driven study
Rossetti G., Milli L., Giannotti F., Pedreschi D.
Genetics and Molecular Biology (all) Settore INF/01 - Informatica Infographics Research and Analysis Methods Commerce Mathematics Biochemistry Statistics (Mathematics) Normal Distribution Marketing Retail Sociology Mathematical and Statistical Techniques Graphs Research Article Multidisciplinary Theoretical Mathematical Models Economics Social Sciences Prediction Communications Probability Theory Agricultural and Biological Sciences (all) Computer and Information Sciences Statistical Methods Forecasting Physical Sciences Model Diffusion of Innovation Data Visualization Innovators Early Adopters Probability Distribution Statistical Data
Innovations are continuously launched over markets, such as new products over the retail market or new artists over the music scene. Some innovations become a success; others don't. Forecasting which innovations will succeed at the beginning of their lifecycle is hard. In this paper, we provide a data-driven, large-scale account of the existence of a special niche among early adopters, individuals that consistently tend to adopt successful innovations before they reach success: we will call them Hit-Savvy. Hit-Savvy can be discovered in very different markets and retain over time their ability to anticipate the success of innovations. As our second contribution, we devise a predictive analytical process, exploiting Hit-Savvy as signals, which achieves high accuracy in the early-stage prediction of successful innovations, far beyond the reach of state-of-the-art time series forecasting models. Indeed, our findings and predictive model can be fruitfully used to support marketing strategies and product placement.
Source: PloS one 12 (2017). doi:10.1371/journal.pone.0189096
Publisher: Public Library of Science, San Francisco, CA , Stati Uniti d'America
1 Haines GH Jr. A theory of market behavior after innovation. Management Science. 1964;10(4):634–658. doi: 10.1287/mnsc.10.4.634
2 Fourt LA, Woodlock JW. Early prediction of market success for new grocery products. The Journal of Marketing. 1960; p. 31–38. doi: 10.2307/1248608
3 Ryan B, Gross NC. The diffusion of hybrid seed corn in two Iowa communities. Rural sociology. 1943;8(1):15.
4 Rogers EM. Diffusion of innovations. Simon and Schuster; 1968-2010.
5 Mellers B, Stone E, Murray T, Minster A, Rohrbaugh N, Bishop M, et al Identifying and cultivating superforecasters as a method of improving probabilistic predictions. Perspectives on Psychological Science. 2015;10(3):267–281. doi: 10.1177/1745691615577794 25987508
6 Tetlock PE, Gardner D. Superforecasting: The art and science of prediction. New York, NY, USA: Crown Publishing Group; 2016.
7 Berndt DJ, Clifford J. Using dynamic time warping to find patterns in time series. In: KDD workshop. vol. 10. Seattle, WA; 1994. p. 359–370.
8 Jeong YS, Jeong MK, Omitaomu OA. Weighted dynamic time warping for time series classification. Pattern Recognition. 2011;44(9):2231–2240. doi: 10.1016/j.patcog.2010.09.022
9 Paparrizos J, Gravano L. k-shape: Efficient and accurate clustering of time series. In: Proceedings of the 2015 ACM SIGMOD International Conference on Management of Data. ACM; 2015. p. 1855–1870.
10 Kleinberg JM. Hubs, authorities, and communities. ACM computing surveys (CSUR). 1999;31(4es):5 doi: 10.1145/345966.345982
11 Tetlock PE, Mellers BA, Scoblic JP. Bringing probability judgments into policy debates via forecasting tournaments. Science. 2017;355(6324):481–483. doi: 10.1126/science.aal3147 28154049
12 DellaVigna S, Pope D. Predicting Experimental Results: Who Knows What? National Bureau of Economic Research; 2016.
13 Moore G. Crossing the chasm: marketing and selling high tech products to mainstream customers. New York: Harper Business Book; 1991.
14 Mahajan V, Peterson RA. Models for innovation diffusion. Newbury Park, California: Sage; 1985.
15 Peterson RA. A note on optimal adopter category determination. Journal of Marketing Research. 1973;10(3):325–329. doi: 10.2307/3149704
16 Mahajan V, Muller E, Srivastava RK. Determination of adopter categories by using innovation diffusion models. Journal of Marketing Research. 1990; p. 37–50. doi: 10.2307/3172549
17 Huh YE, Kim SH. Do early adopters upgrade early? Role of post-adoption behavior in the purchase of next-generation products. Journal of Business Research. 2008;61(1):40–46. doi: 10.1016/j.jbusres.2006.05.007
18 Coscia M. Competition and Success in the Meme Pool: a Case Study on Quickmeme.com. In: ICWSM; 2013.
19 Pennacchioli D, Rossetti G, Pappalardo L, Pedreschi D, Giannotti F, Coscia M. The three dimensions of social prominence. In: International Conference on Social Informatics. Springer; 2013. p. 319–332.
20 Martínez-Torres MR. Application of evolutionary computation techniques for the identification of innovators in open innovation communities. Expert Systems with Applications. 2013;40(7):2503–2510. doi: 10.1016/j.eswa.2012.10.070
21 Saez-Trumper D, Comarela G, Almeida V, Baeza-Yates R, Benevenuto F. Finding trendsetters in information networks. In: Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining. ACM; 2012. p. 1014–1022.
22 Figueiredo F, Almeida JM, Gonçalves MA, Benevenuto F. Trendlearner: Early prediction of popularity trends of user generated content. Information Sciences. 2016;349:172–187. doi: 10.1016/j.ins.2016.02.025
23 Cervellini P, Menezes AG, Mago VK. Finding Trendsetters on Yelp Dataset. In: Computational Intelligence (SSCI), 2016 IEEE Symposium Series on. IEEE; 2016. p. 1–7.
24 Yu S, Christakopoulou E, Gupta A. Identifying decision makers from professional social networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM; 2016. p. 333–342.
25 Mele I, Bonchi F, Gionis A. The early-adopter graph and its application to web-page recommendation. In: Proceedings of the 21st ACM international conference on information and knowledge management. ACM; 2012. p. 1682–1686.
26 Mehmood Y, Barbieri N, Bonchi F. Modeling adoptions and the stages of the diffusion of innovations. vol. 48 Springer; 2016 p. 1–27.
27 Sha X, Quercia D, Dell’Amico M, Michiardi P. Trend makers and trend spotters in a mobile application. In: Proceedings of the 2013 conference on Computer supported cooperative work. ACM; 2013. p. 1365–1374.
28 Condon EM, Golden BL, Wasil EA. Predicting the success of nations at the Summer Olympics using neural networks. Computers & Operations Research. 1999;26(13):1243–1265. doi: 10.1016/S0305-0548(99)00003-9
29 Weissbock J, Inkpen D. Combining textual pre-game reports and statistical data for predicting success in the national hockey league. In: Canadian Conference on Artificial Intelligence. Springer; 2014. p. 251–262.
30 Cintia P, Pappalardo L, Pedreschi D. “Engine Matters”: A First Large Scale Data Driven Study on Cyclists’ Performance. In: 2013 IEEE 13th International Conference on Data Mining Workshops (ICDMW). IEEE; 2013. p. 147–153.
31 Li CT, Hsieh HP, Lin SD, Shan MK. Finding influential seed successors in social networks. In: Proceedings of the 21st International Conference on World Wide Web. ACM; 2012. p. 557–558.
32 Sarigöl E, Pfitzner R, Scholtes I, Garas A, Schweitzer F. Predicting scientific success based on coauthorship networks. EPJ Data Science. 2014;3(1):9 doi: 10.1140/epjds/s13688-014-0009-x
33 Wang D, Song C, Barabási AL. Quantifying long-term scientific impact. Science. 2013;342(6154):127–132. doi: 10.1126/science.1237825 24092745
34 Sinatra R, Wang D, Deville P, Song C, Barabási AL. Quantifying the evolution of individual scientific impact. Science. 2016;354(6312):aaf5239 doi: 10.1126/science.aaf5239 27811240
35 Thorleuchter D, Van Den Poel D. Predicting e-commerce company success by mining the text of its publicly-accessible website. Expert Systems with Applications. 2012;39(17):13026–13034. doi: 10.1016/j.eswa.2012.05.096
36 Krauss J, Nann S, Simon D, Gloor PA, Fischbach K. Predicting Movie Success and Academy Awards through Sentiment and Social Network Analysis. In: ECIS; 2008. p. 2026–2037.
37 Gupta S, Pienta R, Tamersoy A, Chau DH, Basole RC. Identifying successful investors in the startup ecosystem. In: Proceedings of the 24th International Conference on World Wide Web. ACM; 2015. p. 39–40.
38 Zhao XW, Guo Y, He Y, Jiang H, Wu Y, Li X. We know what you want to buy: a demographic-based system for product recommendation on microblogs. In: Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining. ACM; 2014. p. 1935–1944.
39 Ghalwash MF, Radosavljevic V, Obradovic Z. Utilizing temporal patterns for estimating uncertainty in interpretable early decision making. In: Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining. ACM; 2014. p. 402–411.
1. Haines GH Jr. A theory of market behavior after innovation. Management Science. 1964; 10(4):634± 658. https://doi.org/10.1287/mnsc.10.4.634
2. Fourt LA, Woodlock JW. Early prediction of market success for new grocery products. The Journal of Marketing. 1960; p. 31±38. https://doi.org/10.2307/1248608
3. Ryan B, Gross NC. The diffusion of hybrid seed corn in two Iowa communities. Rural sociology. 1943; 8 (1):15.
4. Rogers EM. Diffusion of innovations. Simon and Schuster; 1968-2010.
5. Mellers B, Stone E, Murray T, Minster A, Rohrbaugh N, Bishop M, et al. Identifying and cultivating superforecasters as a method of improving probabilistic predictions. Perspectives on Psychological Science. 2015; 10(3):267±281. https://doi.org/10.1177/1745691615577794 PMID: 25987508
6. Tetlock PE, Gardner D. Superforecasting: The art and science of prediction. New York, NY, USA: Crown Publishing Group; 2016.
7. Berndt DJ, Clifford J. Using dynamic time warping to find patterns in time series. In: KDD workshop. vol. 10. Seattle, WA; 1994. p. 359±370.
8. Jeong YS, Jeong MK, Omitaomu OA. Weighted dynamic time warping for time series classification. Pattern Recognition. 2011; 44(9):2231±2240. https://doi.org/10.1016/j.patcog.2010.09.022
9. Paparrizos J, Gravano L. k-shape: Efficient and accurate clustering of time series. In: Proceedings of the 2015 ACM SIGMOD International Conference on Management of Data. ACM; 2015. p. 1855±1870.
10. Kleinberg JM. Hubs, authorities, and communities. ACM computing surveys (CSUR). 1999; 31(4es):5. https://doi.org/10.1145/345966.345982
11. Tetlock PE, Mellers BA, Scoblic JP. Bringing probability judgments into policy debates via forecasting tournaments. Science. 2017; 355(6324):481±483. https://doi.org/10.1126/science.aal3147 PMID: 28154049
12. DellaVigna S, Pope D. Predicting Experimental Results: Who Knows What? National Bureau of Economic Research; 2016.
13. Moore G. Crossing the chasm: marketing and selling high tech products to mainstream customers. New York: Harper Business Book; 1991.
14. Mahajan V, Peterson RA. Models for innovation diffusion. Newbury Park, California: Sage; 1985.
15. Peterson RA. A note on optimal adopter category determination. Journal of Marketing Research. 1973; 10(3):325±329. https://doi.org/10.2307/3149704
16. Mahajan V, Muller E, Srivastava RK. Determination of adopter categories by using innovation diffusion models. Journal of Marketing Research. 1990; p. 37±50. https://doi.org/10.2307/3172549
17. Huh YE, Kim SH. Do early adopters upgrade early? Role of post-adoption behavior in the purchase of next-generation products. Journal of Business Research. 2008; 61(1):40±46. https://doi.org/10.1016/j. jbusres.2006.05.007
18. Coscia M. Competition and Success in the Meme Pool: a Case Study on Quickmeme.com. In: ICWSM; 2013.
19. Pennacchioli D, Rossetti G, Pappalardo L, Pedreschi D, Giannotti F, Coscia M. The three dimensions of social prominence. In: International Conference on Social Informatics. Springer; 2013. p. 319±332.
20. MartÂõnez-Torres MR. Application of evolutionary computation techniques for the identification of innovators in open innovation communities. Expert Systems with Applications. 2013; 40(7):2503±2510. https://doi.org/10.1016/j.eswa.2012.10.070
21. Saez-Trumper D, Comarela G, Almeida V, Baeza-Yates R, Benevenuto F. Finding trendsetters in information networks. In: Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining. ACM; 2012. p. 1014±1022.
22. Figueiredo F, Almeida JM, GoncËalves MA, Benevenuto F. Trendlearner: Early prediction of popularity trends of user generated content. Information Sciences. 2016; 349:172±187. https://doi.org/10.1016/j. ins.2016.02.025
23. Cervellini P, Menezes AG, Mago VK. Finding Trendsetters on Yelp Dataset. In: Computational Intelligence (SSCI), 2016 IEEE Symposium Series on. IEEE; 2016. p. 1±7.
24. Yu S, Christakopoulou E, Gupta A. Identifying decision makers from professional social networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM; 2016. p. 333±342.
25. Mele I, Bonchi F, Gionis A. The early-adopter graph and its application to web-page recommendation. In: Proceedings of the 21st ACM international conference on information and knowledge management. ACM; 2012. p. 1682±1686.
26. Mehmood Y, Barbieri N, Bonchi F. Modeling adoptions and the stages of the diffusion of innovations. vol. 48. Springer; 2016. p. 1±27.
27. Sha X, Quercia D, Dell'Amico M, Michiardi P. Trend makers and trend spotters in a mobile application. In: Proceedings of the 2013 conference on Computer supported cooperative work. ACM; 2013. p. 1365±1374.
28. Condon EM, Golden BL, Wasil EA. Predicting the success of nations at the Summer Olympics using neural networks. Computers & Operations Research. 1999; 26(13):1243±1265. https://doi.org/10.1016/ S0305-0548(99)00003-9
29. Weissbock J, Inkpen D. Combining textual pre-game reports and statistical data for predicting success in the national hockey league. In: Canadian Conference on Artificial Intelligence. Springer; 2014. p. 251±262.
30. Cintia P, Pappalardo L, Pedreschi D. ªEngine Mattersº: A First Large Scale Data Driven Study on Cyclists' Performance. In: 2013 IEEE 13th International Conference on Data Mining Workshops (ICDMW). IEEE; 2013. p. 147±153.
31. Li CT, Hsieh HP, Lin SD, Shan MK. Finding influential seed successors in social networks. In: Proceedings of the 21st International Conference on World Wide Web. ACM; 2012. p. 557±558.
32. SarigoÈl E, Pfitzner R, Scholtes I, Garas A, Schweitzer F. Predicting scientific success based on coauthorship networks. EPJ Data Science. 2014; 3(1):9. https://doi.org/10.1140/epjds/s13688-014-0009-x 33. Wang D, Song C, BarabaÂsi AL. Quantifying long-term scientific impact. Science. 2013; 342(6154):127± 132. https://doi.org/10.1126/science.1237825 PMID: 24092745
34. Sinatra R, Wang D, Deville P, Song C, BarabaÂsi AL. Quantifying the evolution of individual scientific impact. Science. 2016; 354(6312):aaf5239. https://doi.org/10.1126/science.aaf5239 PMID: 27811240
35. Thorleuchter D, Van Den Poel D. Predicting e-commerce company success by mining the text of its publicly-accessible website. Expert Systems with Applications. 2012; 39(17):13026±13034. https://doi. org/10.1016/j.eswa.2012.05.096
36. Krauss J, Nann S, Simon D, Gloor PA, Fischbach K. Predicting Movie Success and Academy Awards through Sentiment and Social Network Analysis. In: ECIS; 2008. p. 2026±2037.
37. Gupta S, Pienta R, Tamersoy A, Chau DH, Basole RC. Identifying successful investors in the startup ecosystem. In: Proceedings of the 24th International Conference on World Wide Web. ACM; 2015. p. 39±40.
38. Zhao XW, Guo Y, He Y, Jiang H, Wu Y, Li X. We know what you want to buy: a demographic-based system for product recommendation on microblogs. In: Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining. ACM; 2014. p. 1935±1944.
39. Ghalwash MF, Radosavljevic V, Obradovic Z. Utilizing temporal patterns for estimating uncertainty in interpretable early decision making. In: Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining. ACM; 2014. p. 402±411.
2 Fourt LA, Woodlock JW. Early prediction of market success for new grocery products. The Journal of Marketing. 1960; p. 31–38. doi: 10.2307/1248608
3 Ryan B, Gross NC. The diffusion of hybrid seed corn in two Iowa communities. Rural sociology. 1943;8(1):15.
4 Rogers EM. Diffusion of innovations. Simon and Schuster; 1968-2010.
5 Mellers B, Stone E, Murray T, Minster A, Rohrbaugh N, Bishop M, et al Identifying and cultivating superforecasters as a method of improving probabilistic predictions. Perspectives on Psychological Science. 2015;10(3):267–281. doi: 10.1177/1745691615577794 25987508
6 Tetlock PE, Gardner D. Superforecasting: The art and science of prediction. New York, NY, USA: Crown Publishing Group; 2016.
7 Berndt DJ, Clifford J. Using dynamic time warping to find patterns in time series. In: KDD workshop. vol. 10. Seattle, WA; 1994. p. 359–370.
8 Jeong YS, Jeong MK, Omitaomu OA. Weighted dynamic time warping for time series classification. Pattern Recognition. 2011;44(9):2231–2240. doi: 10.1016/j.patcog.2010.09.022
9 Paparrizos J, Gravano L. k-shape: Efficient and accurate clustering of time series. In: Proceedings of the 2015 ACM SIGMOD International Conference on Management of Data. ACM; 2015. p. 1855–1870.
10 Kleinberg JM. Hubs, authorities, and communities. ACM computing surveys (CSUR). 1999;31(4es):5 doi: 10.1145/345966.345982
11 Tetlock PE, Mellers BA, Scoblic JP. Bringing probability judgments into policy debates via forecasting tournaments. Science. 2017;355(6324):481–483. doi: 10.1126/science.aal3147 28154049
12 DellaVigna S, Pope D. Predicting Experimental Results: Who Knows What? National Bureau of Economic Research; 2016.
13 Moore G. Crossing the chasm: marketing and selling high tech products to mainstream customers. New York: Harper Business Book; 1991.
14 Mahajan V, Peterson RA. Models for innovation diffusion. Newbury Park, California: Sage; 1985.
15 Peterson RA. A note on optimal adopter category determination. Journal of Marketing Research. 1973;10(3):325–329. doi: 10.2307/3149704
16 Mahajan V, Muller E, Srivastava RK. Determination of adopter categories by using innovation diffusion models. Journal of Marketing Research. 1990; p. 37–50. doi: 10.2307/3172549
17 Huh YE, Kim SH. Do early adopters upgrade early? Role of post-adoption behavior in the purchase of next-generation products. Journal of Business Research. 2008;61(1):40–46. doi: 10.1016/j.jbusres.2006.05.007
18 Coscia M. Competition and Success in the Meme Pool: a Case Study on Quickmeme.com. In: ICWSM; 2013.
19 Pennacchioli D, Rossetti G, Pappalardo L, Pedreschi D, Giannotti F, Coscia M. The three dimensions of social prominence. In: International Conference on Social Informatics. Springer; 2013. p. 319–332.
20 Martínez-Torres MR. Application of evolutionary computation techniques for the identification of innovators in open innovation communities. Expert Systems with Applications. 2013;40(7):2503–2510. doi: 10.1016/j.eswa.2012.10.070
21 Saez-Trumper D, Comarela G, Almeida V, Baeza-Yates R, Benevenuto F. Finding trendsetters in information networks. In: Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining. ACM; 2012. p. 1014–1022.
22 Figueiredo F, Almeida JM, Gonçalves MA, Benevenuto F. Trendlearner: Early prediction of popularity trends of user generated content. Information Sciences. 2016;349:172–187. doi: 10.1016/j.ins.2016.02.025
23 Cervellini P, Menezes AG, Mago VK. Finding Trendsetters on Yelp Dataset. In: Computational Intelligence (SSCI), 2016 IEEE Symposium Series on. IEEE; 2016. p. 1–7.
24 Yu S, Christakopoulou E, Gupta A. Identifying decision makers from professional social networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM; 2016. p. 333–342.
25 Mele I, Bonchi F, Gionis A. The early-adopter graph and its application to web-page recommendation. In: Proceedings of the 21st ACM international conference on information and knowledge management. ACM; 2012. p. 1682–1686.
26 Mehmood Y, Barbieri N, Bonchi F. Modeling adoptions and the stages of the diffusion of innovations. vol. 48 Springer; 2016 p. 1–27.
27 Sha X, Quercia D, Dell’Amico M, Michiardi P. Trend makers and trend spotters in a mobile application. In: Proceedings of the 2013 conference on Computer supported cooperative work. ACM; 2013. p. 1365–1374.
28 Condon EM, Golden BL, Wasil EA. Predicting the success of nations at the Summer Olympics using neural networks. Computers & Operations Research. 1999;26(13):1243–1265. doi: 10.1016/S0305-0548(99)00003-9
29 Weissbock J, Inkpen D. Combining textual pre-game reports and statistical data for predicting success in the national hockey league. In: Canadian Conference on Artificial Intelligence. Springer; 2014. p. 251–262.
30 Cintia P, Pappalardo L, Pedreschi D. “Engine Matters”: A First Large Scale Data Driven Study on Cyclists’ Performance. In: 2013 IEEE 13th International Conference on Data Mining Workshops (ICDMW). IEEE; 2013. p. 147–153.
31 Li CT, Hsieh HP, Lin SD, Shan MK. Finding influential seed successors in social networks. In: Proceedings of the 21st International Conference on World Wide Web. ACM; 2012. p. 557–558.
32 Sarigöl E, Pfitzner R, Scholtes I, Garas A, Schweitzer F. Predicting scientific success based on coauthorship networks. EPJ Data Science. 2014;3(1):9 doi: 10.1140/epjds/s13688-014-0009-x
33 Wang D, Song C, Barabási AL. Quantifying long-term scientific impact. Science. 2013;342(6154):127–132. doi: 10.1126/science.1237825 24092745
34 Sinatra R, Wang D, Deville P, Song C, Barabási AL. Quantifying the evolution of individual scientific impact. Science. 2016;354(6312):aaf5239 doi: 10.1126/science.aaf5239 27811240
35 Thorleuchter D, Van Den Poel D. Predicting e-commerce company success by mining the text of its publicly-accessible website. Expert Systems with Applications. 2012;39(17):13026–13034. doi: 10.1016/j.eswa.2012.05.096
36 Krauss J, Nann S, Simon D, Gloor PA, Fischbach K. Predicting Movie Success and Academy Awards through Sentiment and Social Network Analysis. In: ECIS; 2008. p. 2026–2037.
37 Gupta S, Pienta R, Tamersoy A, Chau DH, Basole RC. Identifying successful investors in the startup ecosystem. In: Proceedings of the 24th International Conference on World Wide Web. ACM; 2015. p. 39–40.
38 Zhao XW, Guo Y, He Y, Jiang H, Wu Y, Li X. We know what you want to buy: a demographic-based system for product recommendation on microblogs. In: Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining. ACM; 2014. p. 1935–1944.
39 Ghalwash MF, Radosavljevic V, Obradovic Z. Utilizing temporal patterns for estimating uncertainty in interpretable early decision making. In: Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining. ACM; 2014. p. 402–411.
1. Haines GH Jr. A theory of market behavior after innovation. Management Science. 1964; 10(4):634± 658. https://doi.org/10.1287/mnsc.10.4.634
2. Fourt LA, Woodlock JW. Early prediction of market success for new grocery products. The Journal of Marketing. 1960; p. 31±38. https://doi.org/10.2307/1248608
3. Ryan B, Gross NC. The diffusion of hybrid seed corn in two Iowa communities. Rural sociology. 1943; 8 (1):15.
4. Rogers EM. Diffusion of innovations. Simon and Schuster; 1968-2010.
5. Mellers B, Stone E, Murray T, Minster A, Rohrbaugh N, Bishop M, et al. Identifying and cultivating superforecasters as a method of improving probabilistic predictions. Perspectives on Psychological Science. 2015; 10(3):267±281. https://doi.org/10.1177/1745691615577794 PMID: 25987508
6. Tetlock PE, Gardner D. Superforecasting: The art and science of prediction. New York, NY, USA: Crown Publishing Group; 2016.
7. Berndt DJ, Clifford J. Using dynamic time warping to find patterns in time series. In: KDD workshop. vol. 10. Seattle, WA; 1994. p. 359±370.
8. Jeong YS, Jeong MK, Omitaomu OA. Weighted dynamic time warping for time series classification. Pattern Recognition. 2011; 44(9):2231±2240. https://doi.org/10.1016/j.patcog.2010.09.022
9. Paparrizos J, Gravano L. k-shape: Efficient and accurate clustering of time series. In: Proceedings of the 2015 ACM SIGMOD International Conference on Management of Data. ACM; 2015. p. 1855±1870.
10. Kleinberg JM. Hubs, authorities, and communities. ACM computing surveys (CSUR). 1999; 31(4es):5. https://doi.org/10.1145/345966.345982
11. Tetlock PE, Mellers BA, Scoblic JP. Bringing probability judgments into policy debates via forecasting tournaments. Science. 2017; 355(6324):481±483. https://doi.org/10.1126/science.aal3147 PMID: 28154049
12. DellaVigna S, Pope D. Predicting Experimental Results: Who Knows What? National Bureau of Economic Research; 2016.
13. Moore G. Crossing the chasm: marketing and selling high tech products to mainstream customers. New York: Harper Business Book; 1991.
14. Mahajan V, Peterson RA. Models for innovation diffusion. Newbury Park, California: Sage; 1985.
15. Peterson RA. A note on optimal adopter category determination. Journal of Marketing Research. 1973; 10(3):325±329. https://doi.org/10.2307/3149704
16. Mahajan V, Muller E, Srivastava RK. Determination of adopter categories by using innovation diffusion models. Journal of Marketing Research. 1990; p. 37±50. https://doi.org/10.2307/3172549
17. Huh YE, Kim SH. Do early adopters upgrade early? Role of post-adoption behavior in the purchase of next-generation products. Journal of Business Research. 2008; 61(1):40±46. https://doi.org/10.1016/j. jbusres.2006.05.007
18. Coscia M. Competition and Success in the Meme Pool: a Case Study on Quickmeme.com. In: ICWSM; 2013.
19. Pennacchioli D, Rossetti G, Pappalardo L, Pedreschi D, Giannotti F, Coscia M. The three dimensions of social prominence. In: International Conference on Social Informatics. Springer; 2013. p. 319±332.
20. MartÂõnez-Torres MR. Application of evolutionary computation techniques for the identification of innovators in open innovation communities. Expert Systems with Applications. 2013; 40(7):2503±2510. https://doi.org/10.1016/j.eswa.2012.10.070
21. Saez-Trumper D, Comarela G, Almeida V, Baeza-Yates R, Benevenuto F. Finding trendsetters in information networks. In: Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining. ACM; 2012. p. 1014±1022.
22. Figueiredo F, Almeida JM, GoncËalves MA, Benevenuto F. Trendlearner: Early prediction of popularity trends of user generated content. Information Sciences. 2016; 349:172±187. https://doi.org/10.1016/j. ins.2016.02.025
23. Cervellini P, Menezes AG, Mago VK. Finding Trendsetters on Yelp Dataset. In: Computational Intelligence (SSCI), 2016 IEEE Symposium Series on. IEEE; 2016. p. 1±7.
24. Yu S, Christakopoulou E, Gupta A. Identifying decision makers from professional social networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM; 2016. p. 333±342.
25. Mele I, Bonchi F, Gionis A. The early-adopter graph and its application to web-page recommendation. In: Proceedings of the 21st ACM international conference on information and knowledge management. ACM; 2012. p. 1682±1686.
26. Mehmood Y, Barbieri N, Bonchi F. Modeling adoptions and the stages of the diffusion of innovations. vol. 48. Springer; 2016. p. 1±27.
27. Sha X, Quercia D, Dell'Amico M, Michiardi P. Trend makers and trend spotters in a mobile application. In: Proceedings of the 2013 conference on Computer supported cooperative work. ACM; 2013. p. 1365±1374.
28. Condon EM, Golden BL, Wasil EA. Predicting the success of nations at the Summer Olympics using neural networks. Computers & Operations Research. 1999; 26(13):1243±1265. https://doi.org/10.1016/ S0305-0548(99)00003-9
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BibTeX entry
@article{oai:it.cnr:prodotti:384722,
title = {Forecasting success via early adoptions analysis: a data-driven study},
author = {Rossetti G. and Milli L. and Giannotti F. and Pedreschi D.},
publisher = {Public Library of Science, San Francisco, CA , Stati Uniti d'America},
doi = {10.1371/journal.pone.0189096},
journal = {PloS one},
volume = {12},
year = {2017}
}
0000-0003-3099-3835CNR ExploRA
ISTI Repository
DOIAlso available from
ISTI Repository
DOIAlso available from
