The increasing popularity of electronic messages challenges social science researchers, particularly regarding data representativeness and quality. We propose and evaluate a methodology to create a corpus of Twitter data for a given population by sampling the targeted user population. In our case, the population is the groups of citizens, media and politicians using Twitter in Belgium (French and Dutch languages), Norway and France. We present in particular a machine-learning based methodology that enables the population sampling. We also present a methodology to evaluate the representativeness of our corpus compared to the Full Twitter stream of the targeted population. https://osf.io/gse4k/
The increasing popularity of electronic messages challenges social science researchers, particularly regarding data representativeness and quality. We propose and evaluate a methodology to create a corpus of Twitter data for a given population by sampling the targeted user population. In our case, the population is the groups of citizens, media and politicians using Twitter in Belgium (French and Dutch languages), Norway and France. We present in particular a machine-learning based methodology that enables the population sampling. We also present a methodology to evaluate the representativeness of our corpus compared to the Full Twitter stream of the targeted population. https://osf.io/gse4k/
Policy makers have for many years taken for granted that implementing dedicated structures aimed at supporting start-ups had a positive impact on the development of those start-ups. Indeed, building on insights provided by the literature on National/Regional Innovation Systems (Lundvall 1992; Nelson 1993; Cooke et al. 1998; Asheim et al. 2003) and Incubations mechanisms (Etzkowitz and Leydesdorff, 2000; Hackett and Dilts, 2004; Phan et al., 2005; Bergek and Norman, 2008; Bruneel et al, 2012) policy makers perceive such "business incubators" as a way to promote regional economic growth through the support of young and innovative firms. The underlying assumption is that incubators help young firms to develop their innovative products, commercialize them, and maintain a sustainable growth during the start-up period. As a consequence local policy makers have been and still are very enthusiastic in establishing and/or granting financial supports to incubators. For example, the National Business Incubation Association estimates that the number of incubators in the United States has increased from 12 in 1980 to 1,250 in 2012 and that there were about 7,000 such structures across the world. However, there is surprisingly little empirical validation of the positive impact of those structures, with few or no evaluation of their long term performance in helping incubated firms (Vanderstraeten et al., 2014; Schwartz, 2011). As a result, there is evidence of potential political pressure to keep some results under the radar (Fromhold-Eisebith and Eisebith, 2008; Amezcua, 2010; Bergek and Normann, 2008; Mian, 1997). Indeed, some studies indicate that among the new firms incubated, few became fast-growing firms and/or reached the status of middle-sized companies (Schwartz, 2011; Amezcua, 2010; Barringer, Jones and Neubaum, 2005; Phan, Siegel, and Wright, 2005). Besides the above-mentioned mixed research results (Amezcua, 2010; Phan, Siegel, and Wright, 2005), related methodological, theoretical, and empirical limitations (Yu and Nijkamp, 2009) raise criticism about incubator performance studies. Furthermore, those studies rely upon either quantitative (accounting) data available about the incubated start-ups to measure their development, or the qualitative coding and interpretation of interviews using standard content analysis techniques (Lincoln and Guba, 1985). (See for example Barringer, Jones and Neubaum, 2005, with both suffering from the typical limitations of such approaches). In particular it remains largely unknown whether incubation really helps overcoming the resource deficiencies that young firms face in the first years after their market entry (Hannan and Freeman 1984; Stinchcomb, 1965). Overall, an assessment of public policies regarding incubators is still needed (Bergek and Norrman, 2008; OECD, 2006; Mian, 1996a; Mian, 1997). Moreover, there are few studies investigating the impact of incubation on incubated ventures' performance from their own point of view (Fromhold-Eisebith and Eisebith, 2008; Schwartz, 2013; Vanderstraeten et al., 2014). As a consequence, research call for more qualitative bottom-up approach (see Diez, 2001) to explore the impact of incubators' support on incubated firms. In particular, how do incubators create a context that helps firms bring together the various resources they need (Hellmann & Puri, 2002)?
Policy makers have for many years taken for granted that implementing dedicated structures aimed at supporting start-ups had a positive impact on the development of those start-ups. Indeed, building on insights provided by the literature on National/Regional Innovation Systems (Lundvall 1992; Nelson 1993; Cooke et al. 1998; Asheim et al. 2003) and Incubations mechanisms (Etzkowitz and Leydesdorff, 2000; Hackett and Dilts, 2004; Phan et al., 2005; Bergek and Norman, 2008; Bruneel et al, 2012) policy makers perceive such "business incubators" as a way to promote regional economic growth through the support of young and innovative firms. The underlying assumption is that incubators help young firms to develop their innovative products, commercialize them, and maintain a sustainable growth during the start-up period. As a consequence local policy makers have been and still are very enthusiastic in establishing and/or granting financial supports to incubators. For example, the National Business Incubation Association estimates that the number of incubators in the United States has increased from 12 in 1980 to 1,250 in 2012 and that there were about 7,000 such structures across the world. However, there is surprisingly little empirical validation of the positive impact of those structures, with few or no evaluation of their long term performance in helping incubated firms (Vanderstraeten et al., 2014; Schwartz, 2011). As a result, there is evidence of potential political pressure to keep some results under the radar (Fromhold-Eisebith and Eisebith, 2008; Amezcua, 2010; Bergek and Normann, 2008; Mian, 1997). Indeed, some studies indicate that among the new firms incubated, few became fast-growing firms and/or reached the status of middle-sized companies (Schwartz, 2011; Amezcua, 2010; Barringer, Jones and Neubaum, 2005; Phan, Siegel, and Wright, 2005). Besides the above-mentioned mixed research results (Amezcua, 2010; Phan, Siegel, and Wright, 2005), related methodological, theoretical, and empirical limitations (Yu and Nijkamp, 2009) raise criticism about incubator performance studies. Furthermore, those studies rely upon either quantitative (accounting) data available about the incubated start-ups to measure their development, or the qualitative coding and interpretation of interviews using standard content analysis techniques (Lincoln and Guba, 1985). (See for example Barringer, Jones and Neubaum, 2005, with both suffering from the typical limitations of such approaches). In particular it remains largely unknown whether incubation really helps overcoming the resource deficiencies that young firms face in the first years after their market entry (Hannan and Freeman 1984; Stinchcomb, 1965). Overall, an assessment of public policies regarding incubators is still needed (Bergek and Norrman, 2008; OECD, 2006; Mian, 1996a; Mian, 1997). Moreover, there are few studies investigating the impact of incubation on incubated ventures' performance from their own point of view (Fromhold-Eisebith and Eisebith, 2008; Schwartz, 2013; Vanderstraeten et al., 2014). As a consequence, research call for more qualitative bottom-up approach (see Diez, 2001) to explore the impact of incubators' support on incubated firms. In particular, how do incubators create a context that helps firms bring together the various resources they need (Hellmann & Puri, 2002)?
