Suchergebnisse

Intro -- Preface -- Organization -- Contents - Part I -- Contents - Part II -- Full Papers -- Process Workflow in Crowdsourced Digital Disaster Responses -- Abstract -- 1 Introduction -- 2 Digital Volunteer Communities -- 2.1 Related Studies -- 3 Background and Case Study Context -- 4 Methodology -- 5 Findings -- 5.1 Monitoring -- 5.2 Activation -- 5.3 Listing -- 5.4 Listening and Verification -- 5.5 Amplification -- 5.6 Reporting -- 6 Discussion and Implications -- 6.1 Digital Disaster Response Process Workflow -- 6.2 Practical Implications -- 7 Conclusion -- Appendices -- Appendix 1 -- Appendix 2 -- References -- Transitory and Resilient Salient Issues in Party Manifestos, Finland, 1880s to 2010s -- Abstract -- 1 Introduction -- 2 Theory -- 2.1 Salience Theory -- 2.2 Path Dependence Theory and Critical Junctures Theory -- 2.3 Hypotheses -- 3 Research Material and Research Method -- 4 Results -- 4.1 Probing Hypothesis 1 on Resilient Issues in Party Manifestos -- 4.2 Probing Hypothesis 2 on Changes in Meanings of Words Constituting Issues in Party Manifestos -- 5 Discussion -- References -- Diversity in Online Advertising: A Case Study of 69 Brands on Social Media -- 1 Introduction -- 2 Related Work -- 3 Data and Methodology -- 4 Diversity in Online Ads -- 5 Gap Between Demographics of Ads and Users -- 6 Impact of Gender, Race, and Age on Users' Engagements with Ads -- 7 Limitations and Discussions -- 8 Conclusion -- References -- Communication Based on Unilateral Preference on Twitter: Internet Luring in Japan -- 1 Introduction -- 2 Methods -- 2.1 Network Analysis Between Users -- 2.2 Labeling Luring Reply -- 2.3 Detecting a Subset of Communications Which Includes Luring Reply with a High Probability -- 3 Data -- 4 Results -- 4.1 Structure of User-User Communication Networks -- 4.2 Detecting High-Risk Communications -- 5 Discussion.

"This paper provides a framework for understanding Twitter as a historical source. We address digital humanities scholars to enable the transfer of concepts from traditional source criticism to new media formats, and to encourage the preservation of Twitter as a cultural artifact. Twitter has established itself as a key social media platform which plays an important role in public, real-time conversation. Twitter is also unique as its content is being archived by a public institution (the Library of Congress). In this paper we will show that we still have to assume that much of the contextual information beyond the pure tweet texts is already lost, and propose additional objectives for preservation." (author's abstract)

"More and more researchers want to share research data collected from social media to allow for reproducibility and comparability of results. With this paper we want to encourage them to pursue this aim - despite initial obstacles that they may face. Sharing can occur in various, more or less formal ways. We provide background information that allows researchers to make a decision about whether, how and where to share depending on their specific situation (data, platform, targeted user group, research topic etc.). Ethical, legal and methodological considerations are important for making this decision. Based on these three dimensions we develop a framework for social media sharing that can act as a first set of guidelines to help social media researchers make practical decisions for their own projects. In the long run, different stakeholders should join forces to enable better practices for data sharing for social media researchers. This paper is intended as our call to action for the broader research community to advance current practices of data sharing in the future." (author's abstract)

Social Media Monitoring des Bundestagswahlkampfs 2017

Dieser Datensatz enthält Ergebnisse aus dem Social Media Monitoring von Facebook und Twitter für den Bundestagswahlkampf 2017. Das Projekt sammelte die Tweets und Facebook-Posts von politischen Kandidaten und Organisationen und das Engagement der Nutzer mit diesen Inhalten - Retweets und @-Mentions auf Twitter, Kommentare, Shares und Ähnliches auf Facebook. Schließlich wurden alle Nachrichten auf Twitter gesammelt, die mindestens ein Schlüsselwort zu zentralen politischen Themen enthalten. Alle Daten waren zum Zeitpunkt der Datenerhebung öffentlich zugänglich. Die gesammelten Daten sind Eigentum von Facebook und Twitter. Aus diesem Grund und im Hinblick auf die Datenschutzbestimmungen können nur die folgenden Aspekte der Daten weitergegeben werden:

(1) Eine Liste aller Kandidaten, die im Projekt berücksichtigt wurden, ihre Schlüsselattribute und die Identifikation ihrer jeweiligen Twitter-Accounts und Facebook-Seiten.

Kandidatendatensatz: Vor- und Nachname des Kandidaten, akademischer Titel und Namenszusatz falls vorhanden; URL des ersten Facebook-Accounts; URL des zweiten Facebook-Accounts; URL des Twitter-Accounts; Kandidat ist auf einer Parteiliste gelistet; Listenplatz; Direktkandidat in einem der Wahlkreise; Wahlkreisnummer; Name des Wahlkreises; Bundesland; Kandidat ist Mitglied des Bundestages; Parteizugehörigkeit; Geschlecht; Alter (Geburtsjahr); Wohnort; Geburtsort; Beruf.
Zusätzlich verkodet wurde: Eindeutige ID.

(2) Listen von Organisationen, die während eines Wahlkampfes relevant sind, d.h. politische Parteien und wichtige Gatekeeper, mit ihren jeweiligen Twitter- und Facebook-Accounts.

(3) Eine Liste von Tweet-IDs, die verwendet werden können, um die Tweets abzurufen, die wir während unseres Forschungszeitraums gesammelt haben.

Artificial Intelligence (AI)-based systems are widely employed nowadays to make decisions that have far-reaching impact on individuals and society. Their decisions might affect everyone, everywhere, and anytime, entailing concerns about potential human rights issues. Therefore, it is necessary to move beyond traditional AI algorithms optimized for predictive performance and embed ethical and legal principles in their design, training, and deployment to ensure social good while still benefiting from the huge potential of the AI technology. The goal of this survey is to provide a broad multidisciplinary overview of the area of bias in AI systems, focusing on technical challenges and solutions as well as to suggest new research directions towards approaches well-grounded in a legal frame. In this survey, we focus on data-driven AI, as a large part of AI is powered nowadays by (big) data and powerful machine learning algorithms. If otherwise not specified, we use the general term bias to describe problems related to the gathering or processing of data that might result in prejudiced decisions on the bases of demographic features such as race, sex, and so forth. This article is categorized under: Commercial, Legal, and Ethical Issues > Fairness in Data Mining Commercial, Legal, and Ethical Issues > Ethical Considerations Commercial, Legal, and Ethical Issues > Legal Issues. © 2020 The Authors. WIREs Data Mining and Knowledge Discovery published by Wiley Periodicals, Inc.