Charting the Constellation of Science Reform

Over the past decade, a sense of urgency has been building in the scientific community. They have discovered that much of the literature body is unreliable and possibly invalid thanks to weak theory, flawed methods, and shoddy statistics. This is driven by a widespread competitive, secretive approach to research, which, in turn, is fueled by toxic academic incentive structures. Many in the community have decided to address these issues, coming together in what has become known as the ‘scientific reform movement’. While these ‘reformers’ are often spoken of a single, homogeneous entity, my findings underscore the heterogeneity of the reform community. In my dissertation, I explore the scientific reform group using ethnography and social network analysis tools. I primarily studied their online Twitter engagements to understand their culture, practices, and structure. With Wenger’s Community of Practice theory as an interpretive framework, I analyze scientific reform discourse playing out between reformers on Twitter. Using quantitative Twitter friend/follow data, I investigate which reform members engage online, using following behavior to understand aspects of their social structure. I link the quantitative exploration with my qualitative analysis, to conclude that while the reformers are united by their interest in improving science, they are better characterized as a constellation of small communities of practice, each with their own norms, priorities, and unique approach to the group enterprise of scientific reform. My investigation is an exercise in reflexivity as I have studied a community in which I am an active part

An analysis of emotion-exchange motifs in multiplex networks during emergency events

In this paper, we present an analysis of the emotion-exchange patterns that arise from Twitter messages sent during emergency events. To this end, we performed a systematic structural analysis of the multiplex communication network that we derived from a data-set including more than 1.9 million tweets that have been sent during five recent shootings and terror events. In order to study the local communication structures that emerge as Twitter users directly exchange emotional messages, we propose the concept of emotion-exchangemotifs. Our findings suggest that emotion-exchange motifs which contain reciprocal edges (indicating online conversations) only emerge when users exchange messages that convey anger or fear, either in isolation or in any combination with another emotion. In contrast, the expression of sadness, disgust, surprise, as well as any positive emotion are rather characteristic for emotion-exchange motifs representing one-way communication patterns (instead of online conversations). Among other things, we also found that a higher structural similarity exists between pairs of network layers consisting of one high-arousal emotion and one low-arousal emotion, rather than pairs of network layers belonging to the same arousal dimension

Questions And Answers: Exploring Mobile User Needs

The users of mobile devices increasingly use networked services to address their information needs. Questions asked by mobile users are strongly influenced by context factors, such as location and user activity. However in research which has empirically documented the link between mobile information needs and context factors, information about expected answers is scant. Therefore, the goal of this study is to explore the context factors which influence the mobile information needs and the answers expected by mobile users. The results, are obtained by analysing information from paper diaries and digital diaries. This project involved a user study, comprising two different types of studies concerning a paper diary and a digital diary. The analysis of both the paper diary and the digital diary was conducted through grounded theory and taxonomy of information needs. our results indicate a relationship between mobile information needs and context factors and expected answers. Our study explored this relationship between mobile information needs and context factors, and provides a better understanding of the expected answers related to mobile information needs

Post-Feminism, Shaming, and Wedding-Themed Reality Television

This project combines elements of textual analysis, feminist criticism, and media reception studies to examine wedding-themed reality television programming. Drawing on feminist media studies, television studies, and new media studies, this project investigates identity construction through wedding-themed reality television in three case studies: the renegotiation of icons of traditional femininity on Say Yes to the Dress, the policing of female behavior and perceived unruliness through Bridezillas, and the depiction of female labor in celebrity culture through three weddings featured on Keeping Up with the Kardashians. These three case studies deal with unique yet ultimately interconnected themes of gender identity construction and management. I argue that post-feminist ideologies are instrumental in shaping the way that identity is constructed through advocating specific behaviors and shaming others in three key areas: hyper-consumerism, the pursuit of pseudo-celebrity status, and the reinforcement of traditional gender norms. These themes appear in varied forms and function in different ways across the three case studies. In addition, shaming is enacted in the programs and displayed in the audience response to those programs via social media in three ways: subtle discouragement, containment, and pseudo-resistance. This study begins with a close reading of the three television programs, followed by a reception study of the related conversations taking place on the social media platform Twitter to examine how the textual themes are being understood and discussed by viewers

Analysis of Online Social Networks for the Design of Cyber-Physical Mobile Social Networking Services

L'utilizzo di Online Social Network (OSN) e la diffusione di dispositivi mobili stanno cambiando le modalità con cui le persone interagiscono fra loro. I dispositivi mobili, dotati di svariati sensori (fotocamera, GPS, accelerometro, ecc.), permettono la creazione di contenuti con informazioni relative al mondo fisico che circonda l'utente. I contenuti sono poi trasferiti verso il mondo virtuale e condivisi con altri utenti, grazie a servizi come le OSN. Il forte legame che si sta instaurando fra questi due mondi genera nuove opportunità per i sistemi di comunicazione. Ad esempio, le interfacce wireless dei dispositivi possono essere utilizzate per abilitare la comunicazione diretta fra persone in prossimità. Basandosi su questa tipologia di comunicazione, le Mobile Social Network (MSN) sono nate per estendere le OSN con il supporto a reti opportunistiche. Lo studio delle proprietà delle OSN può rivelare schemi di comunicazione, abitudini degli utenti ecc. utili all'ottimizzazione della comunicazione nelle MSN. Ad esempio, un'elevata frequenza di interazione fra gli utenti nelle OSN può rivelare contatti frequenti nel mondo fisico, canale principale per la comunicazione su MSN. Selezionando come mezzo di trasporto i nodi che incontrano più nodi sul loro cammino è possibile diffondere le informazioni efficientemente, limitando il traffico di rete. Una conoscenza approfondita delle proprietà delle OSN può perciò dare un contributo significativo allo sviluppo di MSN. Le proprietà delle reti personali degli utenti delle OSN non sono però note in letteratura, il che risulta un limite per la progettazione di MSN. Ulteriore limitazione è la mancanza di una piattaforma comune in grado di donare alle MSN un insieme di funzionalità di base, come servizi di accesso a comunicazione su rete opportunistica, collezionamento ed elaborazione di dati di contesto e sociali e gestione di dati di sensori. Questa tesi presenta un'analisi dettagliata delle proprietà strutturali delle OSN. I risultati rappresentano le basi per la creazione di MSN future, che possano considerare aspetti sociali degli utenti per l'ottimizzazione della comunicazione e la personalizzazione dei servizi. Inoltre, questa tesi presenta una nuova piattaforma middleware per dispositivi mobili, chiamata CAMEO, che dona una serie di funzionalità di comunicazione su rete opportunistica e facilita la gestione di dati di contesto e sociali. CAMEO rappresenta un supporto concreto allo sviluppo di MSN

Scaling Research Support for Early-Stage Researchers with Crowdsourcing

Support from peers and experts, such as feedback on research artefacts, is an important component of developing research skills. The support is especially helpful for early-stage researchers (ESRs), typically PhD students at the critical stage of learning research skills. Currently, such support mainly comes from a small circle of advisors and colleagues. Gaining access to quality and diverse support outside a research group is challenging for most ESRs. This thesis presents several studies to advance the fundamental and practical understanding of designing systems to scale support for research skills development for ESRs. First, we conduct a systematic literature review on crowdsourcing for education that summarizes existing efforts in the research and application domain. This study also highlights the need for studies on crowdsourcing support for research skills development. Then, based on findings from the first study, we conducted another systematic literature review study on crowdsourcing support for project-based learning and research skills development. The third study explores the qualitative empirical understanding of how ESRs leverage current socio-technical affordances for distributed support in their research activities. This study reveals opportunities afforded by socio-technical systems and challenges faced by ESRs when seeking and adopting support from online research communities. The fourth study explores quantitative empirical understandings of the most desired types of feedback from external researchers that need to be prioritized to offer, and the challenges that need to be prioritized to solve. Building on the findings from the four studies above, we proposed a theoretical framework -- Researchersourcing -- that guides the understanding and designing of socio-technical systems that scale the support for research skills development. Accordingly, in the fifth study, we design and evaluate a crowdsourcing pipeline and a system to scale feedback on research drafts and ease the burdens of reviewing research drafts

Mock Politeness in English and Italian: A Corpus-assisted Study of the Metalanguage of Sarcasm and Irony.

This thesis represents the first in-depth analysis of mock politeness, bringing together research from different academic fields and investigating a range of first-order metapragmatic labels. The investigation is based on a corpus of c. 96 million words taken from two online forums, one based in the UK and one in Italy. For the analysis, I combine corpus linguistics and more traditional qualitative approaches. A key aspect to the analytic process is that it is led by participant understandings of mock politeness and so I take a bottom-up approach to filling some of the gaps in the field. The research aims to tackle three questions. The first addresses which metapragmatic labels are used to refer to mock politeness in the (British) English and Italian data. In the second question, I ask how these metapragmatic labels and the behaviours which they describe relate to one another within and across languages. In the third question, I ask what is the relationship between (a) the English and Italian first-order uses of these metapragmatic labels and the behaviours which they describe and (b) the second order descriptions. In this regard, the use of data from two different cultures is important because it provides an opportunity to investigate to what extent the existing theory accounts for behaviours in different contexts. The findings show that mock politeness cannot be equated with sarcasm, and that the metapragmatic label which may be applied to a mock polite interaction depends on a range of contextual factors, including the participation role of the evaluator and gender of the performer. The range of metapragmatic labels and realisation of mock politeness vary across the two sub-corpora, and the research showed that mock politeness is both structurally and functionally more varied than anticipated by the existing literature

Web Relation Extraction with Distant Supervision

Being able to find relevant information about prominent entities quickly is the main reason to use a search engine. However, with large quantities of information on the World Wide Web, real time search over billions of Web pages can waste resources and the end user’s time. One of the solutions to this is to store the answer to frequently asked general knowledge queries, such as the albums released by a musical artist, in a more accessible format, a knowledge base. Knowledge bases can be created and maintained automatically by using information extraction methods, particularly methods to extract relations between proper names (named entities). A group of approaches for this that has become popular in recent years are distantly supervised approaches as they allow to train relation extractors without text-bound annotation, using instead known relations from a knowledge base to heuristically align them with a large textual corpus from an appropriate domain. This thesis focuses on researching distant supervision for the Web domain. A new setting for creating training and testing data for distant supervision from the Web with entity-specific search queries is introduced and the resulting corpus is published. Methods to recognise noisy training examples as well as methods to combine extractions based on statistics derived from the background knowledge base are researched. Using co-reference resolution methods to extract relations from sentences which do not contain a direct mention of the subject of the relation is also investigated. One bottleneck for distant supervision for Web data is identified to be named entity recognition and classification (NERC), since relation extraction methods rely on it for identifying relation arguments. Typically, existing pre-trained tools are used, which fail in diverse genres with non-standard language, such as the Web genre. The thesis explores what can cause NERC methods to fail in diverse genres and quantifies different reasons for NERC failure. Finally, a novel method for NERC for relation extraction is proposed based on the idea of jointly training the named entity classifier and the relation extractor with imitation learning to reduce the reliance on external NERC tools. This thesis improves the state of the art in distant supervision for knowledge base population, and sheds light on and proposes solutions for issues arising for information extraction for not traditionally studied domains

Hybrid intelligence for data mining

Today, enormous amount of data are being recorded in all kinds of activities. This sheer size provides an excellent opportunity for data scientists to retrieve valuable information using data mining techniques. Due to the complexity of data in many neoteric problems, one-size-fits-all solutions are seldom able to provide satisfactory answers. Although the studies of data mining have been active, hybrid techniques are rarely scrutinized in detail. Currently, not many techniques can handle time-varying properties while performing their core functions, neither do they retrieve and combine information from heterogeneous dimensions, e.g., textual and numerical horizons. This thesis summarizes our investigations on hybrid methods to provide data mining solutions to problems involving non-trivial datasets, such as trajectories, microblogs, and financial data. First, time-varying dynamic Bayesian networks are extended to consider both causal and dynamic regularization requirements. Combining with density-based clustering, the enhancements overcome the difficulties in modeling spatial-temporal data where heterogeneous patterns, data sparseness and distribution skewness are common. Secondly, topic-based methods are proposed for emerging outbreak and virality predictions on microblogs. Complicated models that consider structural details are popular while others might have taken overly simplified assumptions to sacrifice accuracy for efficiency. Our proposed virality prediction solution delivers the benefits of both worlds. It considers the important characteristics of a structure yet without the burden of fine details to reduce complexity. Thirdly, the proposed topic-based approach for microblog mining is extended for sentiment prediction problems in finance. Sentiment-of-topic models are learned from both commentaries and prices for better risk management. Moreover, previously proposed, supervised topic model provides an avenue to associate market volatility with financial news yet it displays poor resolutions at extreme regions. To overcome this problem, extreme topic model is proposed to predict volatility in financial markets by using supervised learning. By mapping extreme events into Poisson point processes, volatile regions are magnified to reveal their hidden volatility-topic relationships. Lastly, some of the proposed hybrid methods are applied to service computing to verify that they are sufficiently generic for wider applications