Content Analysis has been developed within communication science as a technique to analyze bodies of text for features or (recurring) themes, in order to identify cultural indicators, societal trends and issues. And while Content Analysis has seen a tremendous uptake across scientific disciplines, the advent of digital media has presented new challenges to the demarcation and study of content. Within Content Analysis, different strategies have been put forward to grapple with these dynamics. And although these approaches each present ways forward for the analysis of web content, they do not yet regard the vast differences between web platforms that serve content, which each have their own ‘technicities,’ e.g. carry their own (often visually undisclosed) formats and formatting, and output their own results and rankings. In this dissertation I therefore develop Networked Content Analysis as a term for such techniques of Content Analysis that are adapted specifically to the study of networked digital media content. The case in question is climate change, one of the major societal challenges of our times, which I study on the web and with search engines, on Wikipedia as well as Twitter. In all, my contribution provides footing for a return to the roots of Content Analysis and at the same time adds to its toolkit the necessary web- and platform-specific research techniques for creating a fine-grained picture of the climate change debate as it takes place across platforms.
MULTIFILE
Climate change is one of the key societal challenges of our times, and its debate takes place across scientific disciplines and into the public realm, traversing platforms, sources, and fields of study. The analysis of such mediated debates has a strong tradition, which started in communication science and has since then been applied across a wide range of academic disciplines.So-called ‘content analysis’ provides a means to study (mass) media content in many media shapes and formats to retrieve signs of the zeitgeist, such as cultural phenomena, representation of certain groups, and the resonance of political viewpoints. In the era of big data and digital culture, in which websites and social media platforms produce massive amounts of content and network this through hyperlinks and social media buttons, content analysis needs to become adaptive to the many ways in which digital platforms and engines handle content.This book introduces Networked Content Analysis as a digital research approach, which offers ways forward for students and researchers who want to work with digital methods and tools to study online content. Besides providing a thorough theoretical framework, the book demonstrates new tools and methods for research through case studies that study the climate change debate with search engines, Twitter, and the encyclopedia project of Wikipedia.
MULTIFILE
Background Single session email consultations in web-based parenting support may be used for a variety of reasons. Parents may be looking for information on developmental needs of children, for suggestions to improve their parenting skills, or for referrals to helpful resources. The way the practitioner meets the needs of parents, choosing a shortterm and text-based approach, has not been analyzed up till now. Objective To determine if and how practitioner response in single session email consultation matches the need of parents. Method A content analysis of single session email consultations (129 questions; 5,997 response sentences) was conducted. Three perspectives on the parent–practitioner communication were distinguished to assess the match between parenting questions and consultations, i.e., the expert oriented, parent oriented and context oriented perspective. Results The parent oriented type is the dominant paradigm in requesting and providing email consultations, with which the other types may be combined. Most consultations showed a mixed perspective with the use of a limited amount of techniques within each perspective. Correlations between the practitioner’s approach and parental expectancies were weak. Conclusions Professionals have a broad approach to email consultation, offering advice of different perspectives, rather than restricting the advice in order to match a prevalent parental need. All proposed textual techniques were observed in email consultations, providing evidence of their feasibility. Since practice of email consultations is relatively new, practitioners may benefit from the proposed systematic approach to writing email consultations, identifying parental need and permitting the use of professional techniques.
Carboxylated cellulose is an important product on the market, and one of the most well-known examples is carboxymethylcellulose (CMC). However, CMC is prepared by modification of cellulose with the extremely hazardous compound monochloracetic acid. In this project, we want to make a carboxylated cellulose that is a functional equivalent for CMC using a greener process with renewable raw materials derived from levulinic acid. Processes to achieve cellulose with a low and a high carboxylation degree will be designed.
The research, supported by our partners, sets out to understand the drivers and barriers to sustainable logistics in port operations using a case study of drone package delivery at Rotterdam Port. Beyond the technical challenges of drone technology as an upcoming technology, it needs to be clarified how drones can operate within a port ecosystem and how they could contribute to sustainable logistics. KRVE (boatmen association), supported by other stakeholders of Rotterdam port, approached our school to conduct exploratory research. Rotterdam Port is the busiest port in Europe in terms of container volume. Thirty thousand vessels enter the port yearly, all needing various services, including deliveries. Around 120 packages/day are delivered to ships/offices onshore using small boats, cars, or trucks. Deliveries can take hours, although the distance to the receiver is close via the air. Around 80% of the packages are up to 20kg, with a maximum of 50kg. Typical content includes documents, spare parts, and samples for chemical analysis. Delivery of packages using drones has advantages compared with traditional transport methods: 1. It can save time, which is critical to port operators and ship owners trying to reduce mooring costs. 2. It can increase logistic efficiency by streamlining operations. 3. It can reduce carbon emissions by limiting the use of diesel engines, boats, cars, and trucks. 4. It can reduce potential accidents involving people in dangerous environments. The research will highlight whether drones can create value (economic, environmental, social) for logistics in port operations. The research output links to key national logistic agenda topics such as a circular economy with the development of innovative logistic ecosystems, energy transition with the reduction of carbon emissions, societal earning potential where new technology can stimulate the economy, digitalization, key enabling technology for lean operations, and opportunities for innovative business models.
The project aim is to improve collusion resistance of real-world content delivery systems. The research will address the following topics: • Dynamic tracing. Improve the Laarhoven et al. dynamic tracing constructions [1,2] [A11,A19]. Modify the tally based decoder [A1,A3] to make use of dynamic side information. • Defense against multi-channel attacks. Colluders can easily spread the usage of their content access keys over multiple channels, thus making tracing more difficult. These attack scenarios have hardly been studied. Our aim is to reach the same level of understanding as in the single-channel case, i.e. to know the location of the saddlepoint and to derive good accusation scores. Preferably we want to tackle multi-channel dynamic tracing. • Watermarking layer. The watermarking layer (how to embed secret information into content) and the coding layer (what symbols to embed) are mostly treated independently. By using soft decoding techniques and exploiting the “nuts and bolts” of the embedding technique as an extra engineering degree of freedom, one should be able to improve collusion resistance. • Machine Learning. Finding a score function against unknown attacks is difficult. For non-binary decisions there exists no optimal procedure like Neyman-Pearson scoring. We want to investigate if machine learning can yield a reliable way to classify users as attacker or innocent. • Attacker cost/benefit analysis. For the various use cases (static versus dynamic, single-channel versus multi-channel) we will devise economic models and use these to determine the range of operational parameters where the attackers have a financial benefit. For the first three topics we have a fairly accurate idea how they can be achieved, based on work done in the CREST project, which was headed by the main applicant. Neural Networks (NNs) have enjoyed great success in recognizing patterns, particularly Convolutional NNs in image recognition. Recurrent NNs ("LSTM networks") are successfully applied in translation tasks. We plan to combine these two approaches, inspired by traditional score functions, to study whether they can lead to improved tracing. An often-overlooked reality is that large-scale piracy runs as a for-profit business. Thus countermeasures need not be perfect, as long as they increase the attack cost enough to make piracy unattractive. In the field of collusion resistance, this cost analysis has never been performed yet; even a simple model will be valuable to understand which countermeasures are effective.
