Visual abstracts to disseminate research on Twitter: a quantitative analysis

The Web has indisputably changed the way researchers share information. Web-based scholarly communication allows to rapidly disseminate research findings, to reach a broader audience, to transversely connect different contents through hypertext linkages, to update and correct texts if needed, and to integrate multimedia materials. Moreover, it allows interactivity and real-time exchange between authors and readers. Such features are even more evident in the context of the so-called Web 2.0, which involves user-generated content, data sharing, and collaborative efforts. The diffusion of social software and web-based applications has lead to a new use of the Web as a platform for generating, re-purposing and consuming scientific content. Social media brought additional advantages and challenges: they help to fulfill the demand for cheap, instant communication in a context of growing collaborative and interdisciplinary research, but they also, for example, add complexity in terms of quantification of the impact of scientific articles. Nevertheless, researchers are now using social media platforms in every phase of the research lifecycle, from identifying opportunities to disseminating findings. In particular, Twitter, the microblogging platform that allows users to post/publish short messages up to 140 (now 280) characters, has emerged as a powerful tool in scholarly communication. Indeed, it connects researchers around the world (both within and outside one\u2019s research field), giving them the chance to communicate and discuss research findings with the rest of the scientific community, to provide and receive post-publication critiques, and to increase the reach and the impact of their work. Recently, also scientific journals adopted social media, and Twitter in particular, to disseminate research findings published on their pages and websites. In the field of biomedical research, this led to the development of new strategies of dissemination..

Discursive intersections of newspapers and policy elites: a case study of genetically modified food in Britain, 1996-2000

This thesis explores the under-researched terrain of policy elite-newspaper engagements and in so doing makes a substantive contribution in formulating an original conceptual framework for understanding how the interactional dynamics of the political-media complex work. This framework is then applied to the GM food row in Britain by asking how contestation emerged, was sustained then subsided in the political-media complex. This reconstructs the processes by which the pro-GM government consensus was challenged by newspapers, conflict escalated to fever pitch, threatening policy elite agenda and was finally negotiated through key compromises. Drawing on a theoretical framework that combines participatory politics, the political-media complex and new risks, the thesis conceptualises interactional dynamics as ‘discursive intersections’. These are shifts in claims and counter-claims that emerge during engagement at the interface of different sets of knowledge, cultures and agenda in the political-media complex. However there is an element of unpredictability in discursive intersections that arises from the paradoxical interdependence-independence of the relationship in the political-media complex; the elective and episodic nature of engagement on particular issues; and the variable form this may take with potential for conflict, negotiation or consensus. Historical and wider argumentative contexts are crucial to how and what form engagement takes place but do not define it. Thus, the trajectory of discursive intersections needs to be explored empirically rather than predetermined theoretically. This is done using a hybrid methodology that draws attention to the dialogical, persuasive nature of discursive intersections. The substantive contribution of the research is the formulating of this alternative framework for the analysis of interactional dynamics and its application to the GM food row in Britain. It does this by exploring how – that is the process in which - engagement emerged, escalated into contestation, was negotiated and then subsided. What emerged were the following findings. (1) Parallel, sustained and conflictual systems of argumentation about risk were developed between media and political elites despite elite consensus, abstract debates and short news cycles. (2) Newspaper contestation was constructed around a deeply ambivalent suspended certainty based on claims that there was no evidence of risk or benefit, harm or safety and demands for elite responsiveness to acute public anxiety over this

The public sphere according to UK stem cell scientists

In this thesis the concept of social representations is made relevant to the study of the ‘public sphere’ according to scientists. This is elaborated by the re-examination of the notion of a ‘consensual’ and a ‘reified universe’ substantiating a more sociopsychological approach in the study of relevant phenomena. Two processes generate social representations of the public: anchoring and objectification. The empirical study investigates the scientists’ views of the public sphere, in relation to public perceptions, media coverage and the regulation of cloning technology. Elite media coverage of the stem cell debate and conversations with stem cell scientists are systematically analysed with multiple methods. Findings are based on 461 news articles that appeared in Nature and Science between 1997 and 2005 and on interviews with 18 U.K based stem cell researchers conducted between February and October 2005. The analysis compares the debate before and after the ‘stem cell war’ of 2002, and typifies a high tension in representing the public sphere, elaborated in metaphors and prevailing arguments. Central elements of the representation assume a strong disassociation of science from the public sphere; peripheral elements operate with a degree of blurring of those same boundaries, which recognises a common project. This representation, while being expressive of its context of production, constitutes a functional response to it

Procedurally Rhetorical Verb-Centric Frame Semantics as a Knowledge Representation for Argumentation Analysis of Biochemistry Articles

The central focus of this thesis is rhetorical moves in biochemistry articles. Kanoksilapatham has provided a descriptive theory of rhetorical moves that extends Swales' CARS model to the complete biochemistry article. The thesis begins the construction of a computational model of this descriptive theory. Attention is placed on the Methods section of the articles. We hypothesize that because authors' argumentation closely follows their experimental procedure, procedural verbs may be the guide to understanding the rhetorical moves. Our work proposes an extension to the normal (i.e., VerbNet) semantic roles especially tuned to this domain. A major contribution is a corpus of Method sections that have been marked up for rhetorical moves and semantic roles. The writing style of this genre tends to occasionally omit semantic roles, so another important contribution is a prototype ontology that provides experimental procedure knowledge for the biochemistry domain. Our computational model employs machine learning to build its models for the semantic roles and rhetorical moves, validated against a gold standard reflecting the annotation of these texts by human experts. We provide significant insights into how to derive these annotations, and as such have contributions as well to the general challenge of producing markups in the domain of biomedical science documents, where specialized knowledge is required

An analysis of stance and voice in research articles across Chinese and British cultures, using the Appraisal Framework

Scholars from Mainland China are increasingly publishing in the medium of English, in order to gain visibility and credibility worldwide. However, the visibility of Chinese scholars in the Social Sciences is strikingly low. Due to the holistic, interpretative, reiterative nature of knowledge in the Social Sciences, writers have to work harder to establish personal credibility through claim-making negotiations, sharing sympathetic understanding and promoting tolerance in their readers (Becher, 1994; Becher & Trowler, 2001; Hyland, 2000). This thesis investigates differences in stance and voice style between scholars from Mainland China and Britain so as to derive new information which might be useful to novice researchers in the Social Sciences (particularly applied linguistics) who intend to publish internationally. A corpus of 30 research articles in applied linguistics was analysed in terms of Appraisal Theory (Martin & White 2005), theory of context (Xu & Nesi, 2017) and genre analysis (Swales 1990, 2004), using the UAM Corpus Tool (O’Donnell 2011). Findings from this analysis suggest that both the Chinese and the British authors are aware of the need to argue for their own opinions and maintain good relationships with their readers, but choose contrasting ways to realize these same purposes. Generally the Chinese authors try to maintain writer-reader relationships by avoiding explicit attitudinal evaluation of the work of others, while the British authors try to maintain writer-reader relationships by toning down or only evoking stance. The Chinese authors argue for their own positions by reinforcing their explicit attitudes, adding multiple references, sharpening the completion of tasks and construing claims as unquestioned, whereas the British authors argue for their own positions by explicitly evaluating people and phenomena. Because the statistically significant differences in stance and voice strategies revealed in this thesis indicate differences between Chinese and British scholars’ argumentative styles, they suggest the need for a new way of perceiving Chinese ethnolinguistic impact on research writing, and might also inform the teaching of academic writing in the social sciences

한국어 텍스트 논증 구조의 자동 분석 연구

학위논문 (석사)-- 서울대학교 대학원 : 언어학과 언어학전공, 2016. 2. 신효필.최근 온라인 텍스트 자료를 이용하여 대중의 의견을 분석하는 작업이 활발히 이루어지고 있다. 이러한 작업에는 주관적 방향성을 갖는 텍스트의 논증 구조와 중요 내용을 파악하는 과정이 필요하며, 자료의 양과 다양성이 급격히 증가하면서 그 과정의 자동화가 불가피해지고 있다. 본 연구에서는 정책에 대한 찬반 의견으로 구성된 한국어 텍스트 자료를 직접 구축하고, 글을 구성하는 기본 단위들 사이의 담화 관계의 유형을 정의하였다. 하나의 맥락 안에서 두 개의 문장 혹은 절이 서로 관계를 갖는지, 관계를 갖는다면 서로 동등한 관계인지, 그렇지 않은 경우 어느 문장(절)이 더 중요한 부분으로서 다른 하나의 지지를 받는지의 기준에 따라 담화 관계를 두 개의 층위로 나누어 이용하였다. 이러한 기본 단위들 사이의 관계는 기계 학습과 규칙 기반 방식을 이용하여 예측된다. 이 때 각 글의 저자가 표현하고자 하는 의도, 자신의 주장을 뒷받침하기 위해 제시하는 근거의 종류, 그리고 그 근거를 이루는 논증 전략 등이 텍스트의 언어적 특징과 함께 중요한 자질로 작용된다. 논증의 전략으로는 예시, 인과, 세부 사항에 대한 설명, 반복 서술, 정정, 배경 지식 제공 등이 관찰되었다. 이들 세부 분류는 담화 관계의 대분류를 구성하고, 그 담화 관계를 예측하는 데 쓰이는 자질의 기반이 되었다. 또한 일부 언어적 자질들은 기존 연구를 참고하여 한국어 자료에 적용할 수 있는 형태로 재구성하였다. 이를 이용하여 한국어 코퍼스를 구축하고 한국어 연구에 특화된 접속사 및 연결어의 목록을 구성하여 자질 목록에 포함시켰다. 이러한 자질들에 기반해서 담화 관계를 예측하는 과정을 이 연구에서 독자적인 모델로서 자동화하여 제안하였다. 예측 실험의 결과를 보면 본 연구에서 정의하여 이용한 자질들은 긍정적인 상호 작용을 통해 담화 관계 예측의 성능을 향상시킨다는 것을 알 수 있었다. 그 중에서도 일부 접속사 및 연결어, 문장 성분의 유무에 따른 의존적인 문장 구조, 그리고 같은 내용을 반복 서술하는지의 여부 등이 특히 예측에 기여하였다. 텍스트를 이루는 기본 단위들 사이에 존재하는 담화 관계들은 서로 연결, 합성되어 텍스트 전체에 대응되는 트리 형태의 논증 구조를 이룬다. 이렇게 얻은 논증 구조에 대해서는, 트리의 가장 위쪽인 루트 노드에 글의 주제문이 위치하고, 그 바로 아래 층위에 해당하는 문장(절)들이 근거로서 가장 중요한 내용을 담고 있다고 가정할 수 있다. 따라서 주제문을 직접적으로 뒷받침하는 문장(절)을 추출하면 글의 중요 내용을 얻게 된다. 이는 곧 텍스트 요약 작업에서 유용하게 쓰이는 방식이 될 수 있다. 또한 주제에 따른 입장 분류나 근거 수집 등 다양한 분야에서도 응용이 가능할 것이다.These days, there is an increased need to analyze mass opinions using on-line text data. These tasks need to recognize the argumentation schemes and main contents of subjective, argumentative writing, and the automatization of the required procedures is becoming indispensable. This thesis constructed the text data using Korean debates on certain political issues, and defined the types of discourse relations between basic units of text segments. The discourse relations are classified into two levels and four subclasses, according to the standards which determine whether the two segments are related to each other in a context, whether the relation is coordinating or subordinating, and which of the two units in a pair is supported by the other as a more important part. The relations between basic text units are predicted based on machine learning and rule-based methods. The features for the prediction of discourse relations include what the author of a text wants to claim and argumentative strategies comprising grounds for the author's claim, using linguistic properties shown in texts. The strategies for argument are observed and subcategorized into Providing Examples, Cause-and-Effects, Explanations in Detail, Restatements, Contrasts, Background Knowledge, and more. These subclasses compose a broader class of discourse relations and became the basis for features used during the classification of the relations. Some linguistic features refer to those of previous studies, they are reconstituted in a revised form which is more appropriate for Korean data. Thus, this study constructed a Korean debate corpus and a list of connectives specialized to deal with Korean texts to include in the experiment features. The automated prediction of discourse relations based on those features is suggested in this study as a unique model of argument mining. According to the results of experiments predicting discourse relations, the features defined and used in this study are observed to improve the performance of prediction tasks through positive interactions with each other. In particular, some explicit connectives, dependent sentence structures based on lack of certain components, and whether the same meanings are restated clearly contributed to the classification tasks. The discourse relations between basic text units are related and combined with each other to comprise a tree-form argumentation structure for the overall document. Regarding the argumentation structure, the topic sentence of the document is located at the root node in the tree, and it is assumed that the nodes of sentences or clauses right below the root node contain the most important contents as grounds for the topic unit. Therefore, extraction of the text segments directly supporting the topic sentence may help in obtaining the important contents in each document. This can be one of the useful methods in text summarization. Additionally, applications to various fields may also be possible, including stance classification of debate texts, extraction of grounds for certain topics, and so on.1 Introduction 1 1.1 Purposes 1 1.1.1 A Study of Korean Texts with Linguistic Cues 1 1.1.2 Detection of Argumentation Schemes in Debate Texts 2 1.1.3 Extraction of Important Content in Argumentation Schemes of Texts 2 1.2 Structure 3 2 Previous Work 5 2.1 Argumentation Mining Tasks 7 2.1.1 Argument Elements 7 2.1.2 Argumentation Schemes 9 2.2 Argumentation Schemes in Various Texts 14 2.2.1 Dialogic vs. Monologic Texts 14 2.2.2 Debate Texts vs. Other Texts 15 2.2.3 Studies in Other Languages 17 2.3 Theoretical Basis 18 2.3.1 Argumentation Theory 18 2.3.2 Discourse Theory 21 3 Identifying Argumentation Schemes in Debate Texts 25 3.1 Data Description 25 3.2 Basic Units 27 3.3 Discourse Relations 29 3.3.1 Strategies for Proving a Claim 29 3.3.2 Definition 35 4 Automatic Identification of Argumentation Schemes 41 4.1 Annotation 41 4.2 Baseline 46 4.3 Proposed Model 50 4.3.1 O vs. X Classification 51 4.3.2 Convergent Relation Rule 61 4.3.3 NN vs. NS vs. SN Classification 65 4.4 Evaluation 67 4.4.1 Measures 67 4.4.2 Results 68 4.5 Discussion 74 4.6 A Pilot Study on English Texts 81 5 Detecting Important Units 87 6 Conclusion 99 Bibliography 103 초록 117Maste

A Personal Research Agent for Semantic Knowledge Management of Scientific Literature

The unprecedented rate of scientific publications is a major threat to the productivity of knowledge workers, who rely on scrutinizing the latest scientific discoveries for their daily tasks. Online digital libraries, academic publishing databases and open access repositories grant access to a plethora of information that can overwhelm a researcher, who is looking to obtain fine-grained knowledge relevant for her task at hand. This overload of information has encouraged researchers from various disciplines to look for new approaches in extracting, organizing, and managing knowledge from the immense amount of available literature in ever-growing repositories. In this dissertation, we introduce a Personal Research Agent that can help scientists in discovering, reading and learning from scientific documents, primarily in the computer science domain. We demonstrate how a confluence of techniques from the Natural Language Processing and Semantic Web domains can construct a semantically-rich knowledge base, based on an inter-connected graph of scholarly artifacts – effectively transforming scientific literature from written content in isolation, into a queryable web of knowledge, suitable for machine interpretation. The challenges of creating an intelligent research agent are manifold: The agent's knowledge base, analogous to his 'brain', must contain accurate information about the knowledge `stored' in documents. It also needs to know about its end-users' tasks and background knowledge. In our work, we present a methodology to extract the rhetorical structure (e.g., claims and contributions) of scholarly documents. We enhance our approach with entity linking techniques that allow us to connect the documents with the Linked Open Data (LOD) cloud, in order to enrich them with additional information from the web of open data. Furthermore, we devise a novel approach for automatic profiling of scholarly users, thereby, enabling the agent to personalize its services, based on a user's background knowledge and interests. We demonstrate how we can automatically create a semantic vector-based representation of the documents and user profiles and utilize them to efficiently detect similar entities in the knowledge base. Finally, as part of our contributions, we present a complete architecture providing an end-to-end workflow for the agent to exploit the opportunities of linking a formal model of scholarly users and scientific publications

Content Selection for Effective Counter-Argument Generation

The information ecosystem of social media has resulted in an abundance of opinions on political topics and current events. In order to encourage better discussions, it is important to promote high-quality responses and relegate low-quality ones. We thus focus on automatically analyzing and generating counter-arguments in response to posts on social media with the goal of providing effective responses. This thesis is composed of three parts. In the first part, we conduct an analysis of arguments. Specifically, we first annotate discussions from Reddit for aspects of arguments and then analyze them for their persuasive impact. Then we present approaches to identify the argumentative structure of these discussions and predict the persuasiveness of an argument. We evaluate each component independently using automatic or manual evaluations and show significant improvement in each. In the second part, we leverage our discoveries from our analysis in the process of generating counter-arguments. We develop two approaches in the retrieve-and-edit framework, where we obtain content using methods created during our analysis of arguments, among others, and then modify the content using techniques from natural language generation. In the first approach, we develop an approach to retrieve counter-arguments by annotating a dataset for stance and building models for stance prediction. Then we use our approaches from our analysis of arguments to extract persuasive argumentative content before modifying non-content phrases for coherence. In contrast, in the second approach we create a dataset and models for modifying content -- making semantic edits to a claim to have a contrasting stance. We evaluate our approaches using intrinsic automatic evaluation of our predictive models and an overall human evaluation of our generated output. Finally, in the third part, we discuss the semantic challenges of argumentation that we need to solve in order to make progress in the understanding of arguments. To clarify, we develop new methods for identifying two types of semantic relations -- causality and veracity. For causality, we build a distant-labeled dataset of causal relations using lexical indicators and then we leverage features from those indicators to build predictive models. For veracity, we build new models to retrieve evidence given a claim and predict whether the claim is supported by that evidence. We also develop a new dataset for veracity to illuminate the areas that need progress. We evaluate these approaches using automated and manual techniques and obtain significant improvement over strong baselines. Finally, we apply these techniques to claims in the domain of household electricity consumption, mining claims using our methods for causal relations and then verifying their truthfulness

Learning Analytics for Academic Writing through Automatic Identification of Meta-discourse

Effective written communication is an essential skill which promotes educational success for undergraduates. Argumentation is a key requirement of successful writing, which is the most common genre that undergraduates have to write particularly in the social sciences. Therefore, when assessing student writing academic tutors look for students’ ability to present and pursue well-reasoned and strong arguments through scholarly argumentation, which is articulated by meta-discourse. Today, there are some natural language processing systems which automatically detect authors’ rhetorical moves in scholarly texts. Hence, when assessing their students’ essays, educators could benefit from the available automated textual analysis which can detect meta-discourse. However, previous work has not shown whether these technologies can be used to analyse student writing reliably. The aim of this thesis therefore has been to understand how automated analysis of meta-discourse in student writing can be used to support tutors’ essay assessment practices. This thesis evaluates a particular language analysis tool, the Xerox Incremental Parser (XIP) as an exemplar of this type of automated technology. The studies presented in this thesis investigates how tutors define the quality of undergraduate writing and suggests key elements that make for good quality student writing in the social sciences, where XIP seems to work best. This thesis also sets out the changes that needs to be made to the XIP and proposes in what ways its output can be delivered to tutors so that they make use of this output to give feedback on student essays. The findings reported also show problems that academic tutors experience in essay assessment, which potentially could be solved by automated support. However, tutors have preconceptions about the use of automated support. The study revealed that tutors want to be assured that they retain the ‘power’ themselves in any decision of using automated support to overcome these preconceptions

Discourse Analysis and Terminology in Languages for Specific Purposes

Aquest importantíssim recull conté estudis i reflexions sobre temes rellevants en la recerca sobre LSP: anglès mèdic, el llenguatge de la publicitat i periodístic, telecomunicacions i terminologia informàtica, llenguatge comercial i jurídic... Malgrat que gran part dels treballs aplegats es refereixen a l'anglès, també hi ha que tracten l'alemany, francès i altres llengües. Conté textos en anglès, francés, portuguès i castellà