Holistic recommender systems for software engineering

The knowledge possessed by developers is often not sufficient to overcome a programming problem. Short of talking to teammates, when available, developers often gather additional knowledge from development artifacts (e.g., project documentation), as well as online resources. The web has become an essential component in the modern developer’s daily life, providing a plethora of information from sources like forums, tutorials, Q&A websites, API documentation, and even video tutorials. Recommender Systems for Software Engineering (RSSE) provide developers with assistance to navigate the information space, automatically suggest useful items, and reduce the time required to locate the needed information. Current RSSEs consider development artifacts as containers of homogeneous information in form of pure text. However, text is a means to represent heterogeneous information provided by, for example, natural language, source code, interchange formats (e.g., XML, JSON), and stack traces. Interpreting the information from a pure textual point of view misses the intrinsic heterogeneity of the artifacts, thus leading to a reductionist approach. We propose the concept of Holistic Recommender Systems for Software Engineering (H-RSSE), i.e., RSSEs that go beyond the textual interpretation of the information contained in development artifacts. Our thesis is that modeling and aggregating information in a holistic fashion enables novel and advanced analyses of development artifacts. To validate our thesis we developed a framework to extract, model and analyze information contained in development artifacts in a reusable meta- information model. We show how RSSEs benefit from a meta-information model, since it enables customized and novel analyses built on top of our framework. The information can be thus reinterpreted from an holistic point of view, preserving its multi-dimensionality, and opening the path towards the concept of holistic recommender systems for software engineering

Analyzing Misinformation Claims During the 2022 Brazilian General Election on WhatsApp, Twitter, and Kwai

This study analyzes misinformation from WhatsApp, Twitter, and Kwai during the 2022 Brazilian general election. Given the democratic importance of accurate information during elections, multiple fact-checking organizations collaborated to identify and respond to misinformation via WhatsApp tiplines and power a fact-checking feature within a chatbot operated by Brazil's election authority, the TSE. WhatsApp is installed on over 99% of smartphones in Brazil, and the TSE chatbot was used by millions of citizens in the run-up to the elections. During the same period, we collected social media data from Twitter (now X) and Kwai (a popular video-sharing app similar to TikTok). Using the WhatsApp, Kwai, and Twitter data along with fact-checks from three Brazilian fact-checking organizations, we find unique claims on each platform. Even when the same claims are present on different platforms, they often differ in format, detail, length, or other characteristics. Our research highlights the limitations of current claim matching algorithms to match claims across platforms with such differences and identifies areas for further algorithmic development. Finally, we perform a descriptive analysis examining the formats (image, video, audio, text) and content themes of popular misinformation claims

Populism, Populist or Personality? What is actually gaining in support and how to test it

Surprise election results around the world - surprising largely due to polls being unable to accurately grasp the mood of the electorate - are fuelling debates such as the supposed rise of populist movements. But what exactly is it that is on the rise? Is it populism – the movement intractably associated with right wing nationalism, hatred and bigotry? Is it populist campaigning, a framing tactic of posing the candidate standing as one with the ordinary people, in opposition to a (stylised) undemocratic and self-serving elite, irrespective of ideology or partisan leaning? Or is it the rise of the personality or celebrity candidate, who appeals personally to voters more than and differently to party or ideology or any message? Election results are not always clear, as a particular candidate may attract voters for all these or other reasons, so trying to interpret meaning from vote data is ambiguous at best. To truly know what is on the rise, we must determine vote causality. This paper will look at the difference between Populism, Populist campaigns, and Personality candidates, examine whether there has been a rise by comparing 2013 and 2016 federal election Senate results, and discuss the best methodological approaches for testing what is driving voters towards these political forces

How much do you care about education? Exploring fluctuations of public interest in education issues among top national priorities in the U.S.

It is well known that a strong education system produces citizens who are more engaged in civil and social duties, with obvious benefits to society and the individuals. Policymakers who have the power to help improve the education system frequently rely on the news or the polls to better understand the issues involved, but these tools are often unable to answer customized questions on the public view with a large enough coverage. Monitoring the American public interest in education over the years is not new. In fact, a number of national polling agencies have tracked education as part of their larger polls asking people to name the most burning issues facing the US. While these polls provide a fair indication of the changes in importance of education in the eyes of the public, they do not identify the factors which have historically been associated with the major fluctuations of such importance. Most importantly, these traditional national polls do not track public concern about specific subtopics within education. This mixed methods study includes the creation of a software instrument with the objective of exploring the salience of education as a national priority over time and analyzing the possible factors associated with these fluctuations of interest. In addition to discovering the most prominent latent subtopics affecting education (such as academic achievement, sexual assault and freedom of speech), this study also seeks national-level issues that may have recently been associated with the largest declines. The only source of data utilized is the text of tens of thousands of published news articles. Terms extracted from the text using natural language processing serve as the basis for automated qualitative analysis. As topics emerge from the data, the frequencies of the terms are utilized to associate the articles with the most relevant ones. The analysis shows that public interest in education has declined the most during election times. It is also found that the areas that contributed the most during the largest surges of public interest in education from 2015 to 2020 were school budget, academic achievement gaps and mental health

The collaborative index

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.Information-seekers use a variety of information stores including electronic systems and the physical world experience of their community. Within electronic systems, information-seekers often report feelings of being lost and suffering from information overload. However, in the physical world they tend not to report the same negative feelings. This work draws on existing research including Collaborative Filtering, Recommender Systems and Social Navigation and reports on a new observational study of information-seeking behaviours. From the combined findings of the research and the observational study, a set of design considerations for the creation of a new electronic interface is proposed. Two new interfaces, the second built from the recommendations of the first, and a supporting methodology are created using the proposed design considerations. The second interface, the Collaborative Index, is shown to allow physical world behaviours to be used in the electronic world and it is argued that this has resulted in an alternative and preferred access route to information. This preferred route is a product of information-seekers' interactions 'within the machine' and maintains the integrity of the source information and navigational structures. The methodology used to support the Collaborative Index provides information managers with an understanding of the information-seekers' needs and an insight into their behaviours. It is argued that the combination of the Collaborative Index and its supporting methodology has provided the capability for information-seekers and information managers to 'enter into the machine', producing benefits for both groups

Political Rhetoric and the Media: The Year in C-SPAN Research, Volume 8

This volume of The Year in C-SPAN Archives Research features analyses of the C-SPAN Video Library, a digital collection of 275,000 hours of indexed videos, texts, and spoken words. Included in this volume are papers on Rev. Jesse Jackson’s presidential campaign, rhetorical analysis of agriculture policy, and an examination of Senator Edward Kennedy’s positions on health care. The text also contains analysis of the “spectacle of committee hearings” and a look at the visuals used in the second Trump impeachment trial

CREATE: Concept Representation and Extraction from Heterogeneous Evidence

Traditional information retrieval methodology is guided by document retrieval paradigm, where relevant documents are returned in response to user queries. This paradigm faces serious drawback if the desired result is not explicitly present in a single document. The problem becomes more obvious when a user tries to obtain complete information about a real world entity, such as person, company, location etc. In such cases, various facts about the target entity or concept need to be gathered from multiple document sources. In this work, we present a method to extract information about a target entity based on the concept retrieval paradigm that focuses on extracting and blending information related to a concept from multiple sources if necessary. The paradigm is built around a generic notion of concept which is defined as any item that can be thought of as a topic of interest. Concepts may correspond to any real world entity such as restaurant, person, city, organization, etc, or any abstract item such as news topic, event, theory, etc. Web is a heterogeneous collection of data in different forms such as facts, news, opinions etc. We propose different models for different forms of data, all of which work towards the same goal of concept centric retrieval. We motivate our work based on studies about current trends and demands for information seeking. The framework helps in understanding the intent of content, i.e. opinion versus fact. Our work has been conducted on free text data in English. Nevertheless, our framework can be easily transferred to other languages

Value Matrix and Domain Map - Boundary Objects for Systemic Innovation

Information overload is a complex and growing problem that many systems have tried to remedy. Recognizing that technology alone will probably not be enough to solve this problem, and that conventional knowledge work practices need to change to take advantage of existing or new tools, Knowledge Federation has self-organized as a community for doing systemic innovation. That is, they work to redesign and change the practices in key areas, such as public informing, education and science, of knowledge work. Knowledge Federation is a transdisciplinary community, which consists of experts and stakeholders from a variety of fields, both technical and non-technical. Thus, the challenge is to provide the enabling technology in such a way that the technical details of the implementation are ``encapsulated'' or hidden, and that exactly those functions that are needed and natural for systemic innovation are ``exported'' or provided. In this thesis, we address this challenge by introducing two initial prototypes for ``boundary objects'', objects that serve as communication channels between two domains. Here, the two domains are the technical domain of tool builders, such as Topic Maps, Semantic Web and various IBIS implementations, and the non-technical domain where systemic innovation takes place. A specific purpose of these objects is to enable the creation of a suitable ``knowledge work ecology'' where the right kind of practices are supported. That is, the ones that are needed to remedy the information overload. The first object is the Domain Map Object (DMO), which can be likened to a filing cabinet, or a place for organizing and storing knowledge resources. It can also be viewed as a map, or a collection of maps, whose purpose is to show a high-level overview of the subject domain so that what is worth seeing can be easily located. In other words, the DMO provides affordances for organizing knowledge, which naturally stimulates the suitable practices. Our other object is the Value Matrix Object (VMO), which is an object attached to every resource in a domain, accumulating all data that can be relevant for computing the value of the associated resource with respect to a given query or context. Our definition of ``resource'' includes users, specifically authors, in addition to knowledge resources. In particular, the VMO provides affordances for rewarding human users for right behavior, such as organizing knowledge resources and taking time to produce high-quality content instead of focusing on quantity, by keeping track of all contributions and their value. Thus, the VMO can be used by system builders to create an ecology that rewards both production of high quality knowledge as well as contribution to knowledge organization. Besides describing the two objects, we design and implement a prototype that shows the objects' main capabilities. We complete the functionality of our objects as boundary objects by inviting people from the two relevant communities to test the prototype and answer a questionnaire. At the same time, this can be seen as an experiment to test the feasibility and usability of our objects. Based on the results of the tests, we give suggestions for improving the present boundary object prototypes