Financement participatif | EPFL Graph Search

Le financement participatif, ou sociofinancement au Canada, est un outil de collecte de fonds fonctionnant sur une plateforme internet et permettant à un ensemble de contributeurs de choisir collectivement de financer directement et de manière traçable des projets identifiés. Les porteurs de projet souhaitant collecter des fonds pour une initiative personnelle, professionnelle, ou bien communautaire, peuvent être des entreprises, des associations, des particuliers, etc. Ce mode de financement se fait sans l'aide des acteurs traditionnels du financement, il est dit désintermédié. L'émergence des plateformes de financement participatif a été permise grâce à internet et aux réseaux sociaux, complétant ou remplaçant la traditionnelle souscription. Cette tendance s'inscrit dans un mouvement plus global : celui de la consommation collaborative et de la production participative. Le financement participatif comprend différents secteurs tels que le don (avec et sans récomp

Financing Biomedical Ventures - Myths and Realities

Jeffrey Scott Behrens

Angel funding is an important source of capital for startup ventures (J. Sohl 2019), providing a similar level of capital as institutional venture capital (OECD 2011). But angel funding is challenging to study due to the informal nature of the activity. A common paradigm for new venture funding argues that new ventures initially fund their activities with angel investment and then, as they show success, make progress to commercialization and de-risk core venture propositions, ventures transition to more professional institutional (venture capital) funding. A belief in this paradigm can strongly influence early venture creation activities, and assumptions about the later availability of capital from institutional venture capital underpin many startup business plans. However, the literature exploring this phenomenon is very sparse, and the question of the relationship of angel to venture funding transition has rarely been studied. By analyzing a large and robust dataset, this thesis advances the understanding of the actual behavior of investors and the impact this early behavior, and the funding choices involved, have on company outcomes. This thesis also explores a novel form of venture investing and places it into a broader context of new venture funding that may help explain some of the key observations discussed. These fundamentally empirical studies examine the nature/reality of important entrepreneurial phenomena of interactions between angel and venture funding in novel ways, pose questions, and identify a set results that point to a potential market failure. Attempts are made to understand these results and place them into the contexts of both research theory and a broader understanding of entrepreneurial behavior; there certainly remain a set of unanswered questions and potential fruitful directions for future research.

EPFL2020

On the use of applied machine learning and digital infrastructure to leverage social media data in health and epidemiology

Martin Mathias Müller

The quantification of population-level health behaviors is crucial for guiding public health policy. However, traditional methods for measuring such health behaviors have several short- comings. In recent years social media data has been successfully used to measure health behaviors and may be used as a low-cost and real-time addition to traditional data sources. Methods from the field of natural language processing are increasingly used to automatically process, filter and categorize the rapidly growing amount of publicly available social media data. However, a number of methodological challenges limit the rate at which we can generate insight from such data. In this work I will argue that long-term investment into digital infrastructure and open source tooling is required in order to overcome these challenges. In chapter 2 we introduce the Crowd- breaks platform which is the basis of this thesis. Crowdbreaks is an open source framework for real-time data collection, continuous crowdsourced annotation, and continuous re-training of machine learning classifiers. In contrast to traditional research workflows, projects on Crowdbreaks run over an extended period of time, allowing for the observation of health trends over multiple years while keeping algorithms up-to-date. In chapter 3 we quantify the occurrence of concept drift in vaccine-related Twitter data, which further validates the need for the Crowdbreaks platform. In chapter 4 we use the Crowdbreaks platform to trace sentiment towards the novel gene-editing technology CRISPR/Cas9 back to its first application in 2013 and investigate how public opinion may have been affected in context of recent scandals sur- rounding the technology. In chapter 5 we turn our attention to the COVID-19 pandemic and analyze who was speaking and who was heard in the early months of the pandemic. Chapter 6 builds on this work and explores the dynamics of Twitter communities during the COVID-19 pandemic. Lastly, in chapter 7 we introduce COVID-Twitter-BERT, a domain-specific language model which has been used in various downstream natural language processing applications on COVID-19-related Twitter data.

EPFL2021

I Am What I Pledge: The Importance of Value Alignment for Mobilizing Backers in Reward-Based Crowdfunding

Julia Katharina Binder

Crowdfunding has emerged as an important alternative financing tool for entrepreneurs. Extant research on the antecedents of crowdfunding success have produced divergent results. By applying the cross-disciplinary lens of strategic linguistic framing, that is, framing campaign messages in a way that is salient and that resonates with the values of the audience, we experimentally examine the role of value framing for a successful campaign outcome. Our results indicate that altruistically framed campaigns have a higher chance for funding compared to campaigns that emphasize egoistic or environmental motives, but even more importantly, that message framing needs to be aligned with the personal values of the backers. As such, our study highlights important similarities between resource mobilization in social movements and in crowdfunding.

2020

On the use of applied machine learning and digital infrastructure to leverage social media data in health and epidemiology

Martin Mathias Müller

EPFL2021