Do users need human-like conversational agents? - Exploring conversational system design using framework of human needs

The fascinating story of human evolution can be attributed to our ability to speak, write, and communicate complex thoughts. When researchers envision a perfect, artificially intelligent conversational system, they want the system to be human-like. In other words, the system should converse with the same intellect and cognition as humans. Now, the question which we need to ask is if we need a human-like conversational system? Before we engage in the complex endeavor of implementing human-like characteristics, we should debate if the pursuit of such a system is logical and ethical. We analyze some of the system-level characteristics and discuss their merits and potential of harm. We review some of the latest work on conversational systems to understand how design features are evolving for Conversational Agents. Additionally, we look into the framework of human needs to assess how the system should assign relative importance to user requests, and prioritize user tasks. We draw on the peer work in human-computer interaction, sentiment analysis, and human psychology to provide insights into how future conversational agents should be designed for better user satisfaction

Toward Automatic Fake News Classification

The interaction of technology with humans have many adverse effects. The rapid growth and outreach of the social media and the Web have led to the dissemination of questionable and untrusted content among a wider audience, which has negatively influenced their lives and judgment. Different election campaigns around the world highlighted how \u27\u27fake news\u27\u27 - misinformation that looks genuine - can be targeted towards specific communities to manipulate and confuse them. Ever since, automatic fake news detection has gained widespread attention from the scientific community. As a result, many research studies have been conducted to tackle the detection and spreading of fake news. While the first step of such tasks would be to classify claims associated based on their credibility, the next steps would involve identifying hidden patterns in style, syntax, and content of such news claims. We provide a comprehensive overview of what has already been done in this domain and other similar fields, and then propose a generalized method based on Deep Neural Networks to identify if a given claim is fake or genuine. By using different features like the authenticity of the source, perceived cognitive authority, style, and content-based factors, and natural language features, it is possible to predict fake news accurately. We have used a modular approach by combining techniques from information retrieval, natural language processing, and deep learning. Our classifier comprises two main sub-modules. The first sub-module uses the claim to retrieve relevant articles from the knowledge base which can then be used to verify the truth of the claim. It also uses word-level features for prediction. The second sub-module uses a deep neural network to learn the underlying style of fake content. Our experiments conducted on benchmark datasets show that for the given classification task we can obtain up to 82.4% accuracy by using a combination of two models; the first model was up to 72% accurate while the second model was around 81\% accurate. Our detection model has the potential to automatically detect and prevent the spread of fake news, thus, limiting the caustic influence of technology in the human lives

Identifying Citation Sentiment and its Influence while Indexing Scientific Papers

Sentiment analysis has proven to be a popular research area for analyzing social media texts, newspaper articles, and product reviews. However, sentiment analysis of citation instances is a relatively unexplored area of research. For scientific papers, it is often assumed that the sentiment associated with citation instances is inherently positive. This assumption is due to the hedged nature of sentiment in citations, which is difficult to identify and classify. As a result, most of the existing indexes focus only on the frequency of citation. In this paper, we highlight the importance of considering the sentiment of citation while preparing ranking indexes for scientific literature. We perform automatic sentiment classification of citation instances on the ACL Anthology collection of papers. Next, we use the sentiment score in addition to the frequency of citation to build a ranking index for this collection of scientific papers. By using various baselines, we highlight the impact of our index on the ACL Anthology collection of papers. Our research contributes toward building more sentiment sensitive ranking index which better underlines the influence and usefulness of research papers

The sound of music: from increased personalization to therapeutic values

Music providers like Spotify leverage music recommendation systems to connect users with relevant music. Based on content-based and collaborative-filtering statistical methods, these machine learning algorithms quantify user-song probabilities and present the highest-ranked songs. However, most music providers do not fully address their users’ music seeking and retrieval needs. Likewise, the fields of Recommender Systems (RecSys), Music Recommendation Systems (MRS) and Music Information Retrieval (MIR) remain disconnected from real-world use cases of music seeking. In this conceptual paper, we review the literature of the RecSys, MRS, MIR and Music Therapy (MT) academic fields. We discuss trends towards greater user control and personalization in the MRS and MIR fields and the connections between MT and positive health outcomes such as reductions in stress, anxiety and heart rate.Analysis. We argue that greater control and visibility into the characteristics of songs and recommended items can generate positive downstream benefits. We recommend features that empower users to better seek, find, store, retrieve and learn from their musical catalogs. We suggest design enhancements that recognize music’s wider psychological and physiological benefits and create opportunities to build domain knowledge. Unlocking music’s myriad benefits through the enhancements proposed would catalyze positive outcomes for business stakeholders, users and society.Peer Reviewe

Understanding Sexual and Gender Minority Privacy

This panel presentation is a summary update of the recent research from the 2021 ALISE Research Grant Competition Award: Educating for Equity?: Sexual and Gender Minority Privacy in Library and Information Studies Education. This session aligns with the conference theme, “Go back and Get It: From One Narrative to Many” by illustrating the ways in which “privacy” is a polysemous concept, and information professionals grappling with privacy from a policy perspective must be prepared for its multi-faceted nature and the distinct privacy needs of different communities. New library and information professionals are expected to uphold a number of ethical principles, often in contexts of incredible complexity, wherein principles may well be in conflict with one another. One of the major tools by which organizations may navigate such conflicts is policy. Indeed, “[f]or current and future information professionals to be truly prepared for the far-reaching impacts of policy on their careers and their institutions, LIS educators need to make a commitment to teaching information policy” (Jaeger et al., 2015, p. 175). That education should approach information policy in a holistic manner, as “an interrelated set of issues that comprise a larger entity” (Jaeger et al., 2015, p. 175), rather than a discrete set of issues such as privacy and intellectual freedom. Educating students in information policy becomes even more urgent when one considers how much of the ecosystem in which users participate through libraries and other information institutions is outside of the direct control of the institution itself, mediated by contracts and policies. In order to explore this larger question of information policy education for LIS students, this project proposed to examine a specific case: library policy addressing the privacy of sexual and gender minority (SGM) people (also often referred to by some variation of LGBTQI2SA+ people). This case sits at the confluence of several areas of discrete policy concern within LIS, including diversity, equity, and inclusion, equal access to information, privacy, and intellectual freedom. Therefore, this project provides a good opportunity to explore if and how LIS students are being equipped to deal with policy questions as “an interrelated set of issues.” This is also a case of incredible urgency, as COVID-19 has deepened existing inequities facing sexual and gender minority people in the United States. This project examined whether students are equipped to handle complex questions of information policy by examining the urgent information problem facing libraries: the privacy of SGM individuals in the face of COVID-19 surveillance. Sexual and gender minorities face significant information risks that differ from those of cis-gender, heterosexual people; improper information disclosure can lead to the loss of employment, housing, access to health care, and social support for SGM due to outing. Because of the risks of outing and discrimination, privacy concerns are of special concern to LGBTQ+ people in the face of pandemic surveillance, particularly digital surveillance. While privacy rights have been a point of controversy and uncertainty for all in the face of digital surveillance and the exigencies of the pandemic, sexual and gender minorities may well struggle to assert even those rights to which they are unquestionably entitled. However, even those who choose to advocate for their communities and assert their rights often do not realize that libraries are a potential place of risk. This study asked, “Are LIS programs preparing their students to meet the needs of LGBTQ+ patrons and stakeholders from a policy perspective in the workplace?” We have answered that question through a mixed-methods study, including a survey of LIS faculty, a content analysis of ALA-approved masters programs’ learning outcomes and syllabi, a document analysis of the privacy policies of a purposive sample of libraries, and interviews with library employers. The panel will share the insights gained from this study in order to expand the understanding of the privacy of marginalized groups within libraries, and provide direction for future research into information policy education within LIS. The panel will also discuss how this research has led to collaborations with other colleagues which focuses on human-centered digital privacy solutions for digital and social media. The burgeoning growth of artificial intelligence (AI) and machine learning (ML) solutions, the ubiquity of social media and digital repositories, and cross-platform data usage have raised the stakes of addressing digital privacy. The enormity of data presents an uphill task to identify and mitigate private information on digital and social media platforms. Lastly, through engagement with participants, probing questions will be asked of the audience to brainstorm and collect ideas. Panel attendees will be active participants, working with the panelists in breakout groups, considering how find privacy solutions that are responsive to the legal and regulatory, social, cultural, and technical dimensions that we encounter within LIS education and beyond