A STUDY ON TRAVEL INFORMATION ADOPTION INTENTION IN THE ONLINE SOCIAL COMMUNITY: THE PERSPECTIVES OF CUSTOMER EXPERIENCE AND INFORMATION ADOPTION MODEL

With the popularity of the online social community, people have become accustomed to sharing their travel experiences online. Internet users can read about others’ experiences, view tour photos, and gather information from other users during their leisure time or before travelling abroad. This study based on the information adoption theory and the concept of experience marketing; additionally, the moderating effects of consumption point on association among customer experiences, information usefulness, and information adoption intentions has been investigated. An Internet survey was conducted for data collection, and 492 returned responses were analyzed. The findings show that customer experience and information usefulness increase Internet users’ information adoption intentions and that the quality and credibility of Internet tourism information have a positive effect on customer experience and information usefulness. Content vividness was linked to an improved user experience. Consumption point influences the relationship between information usefulness and information adoption intentions, but it does not affect the relationship between customer experience and information adoption intention

Twitter Activity Of Urban And Rural Colleges: A Sentiment Analysis Using The Dialogic Loop

The purpose of the present study is to ascertain if colleges are achieving their ultimate communication goals of maintaining and attracting students through their microblogging activity, which according to Dialogic Loop Theory, is directly correlated to the use of positive and negative sentiment. The study focused on a cross-section of urban and rural community colleges within the United States to identify the sentiment score of their microblogging activity. The study included a content analysis on the Twitter activity of these colleges. A data-mining process was employed to collect a census of the tweets associated with these colleges. Further processing was then applied using data linguistic software that removed all irrelevant text, word abbreviations, emoticons, and other Twitter specific classifiers. The resulting data set was then processed through a Multinomial Naive Bayes Classifier, which refers to a probability of word counts in a text. The classifier was trained using a data source of 1.5 million tweets, called Sentiment140, that qualitatively analyzed the corpus of these tweets, labeling them as positive and negative sentiment. The Multinomial Naive Bayes Classifier distinguished specific wording and phrases from the corpus, comparing the data to a specific database of sentiment word identifiers. The sentiment analysis process categorized the text as being positive or negative. Finally, statistical analysis was conducted on the outcome of the sentiment analysis. A significant contribution of the current work was extending Kent and Taylor\u27s (1998) Dialogic Loop Theory, which was designed specifically for identifying the relationship building capabilities of a Web site, to encompass the microblogging concept used in Twitter. Specifically, Dialogic Loop Theory is applied and enhanced to develop a model for social media communication to augment relationship building capabilities, which the current study established as a new form for evaluating Twitter tweets, labeled in the current body of work as Microblog Dialogic Communication. The implication is that by using Microblog Dialogic Communication, a college can address and correct their microblogging sentiment. The results of the data collected found that rural colleges tweeted more positive sentiment tweets and less negative sentiment tweets when compared to the urban colleges tweets

Semantic Sentiment Analysis of Microblogs

Microblogs and social media platforms are now considered among the most popular forms of online communication. Through a platform like Twitter, much information reflecting people's opinions and attitudes is published and shared among users on a daily basis. This has recently brought great opportunities to companies interested in tracking and monitoring the reputation of their brands and businesses, and to policy makers and politicians to support their assessment of public opinions about their policies or political issues. A wide range of approaches to sentiment analysis on Twitter, and other similar microblogging platforms, have been recently built. Most of these approaches rely mainly on the presence of affect words or syntactic structures that explicitly and unambiguously reflect sentiment (e.g., "great'', "terrible''). However, these approaches are semantically weak, that is, they do not account for the semantics of words when detecting their sentiment in text. This is problematic since the sentiment of words, in many cases, is associated with their semantics, either along the context they occur within (e.g., "great'' is negative in the context "pain'') or the conceptual meaning associated with the words (e.g., "Ebola" is negative when its associated semantic concept is "Virus"). This thesis investigates the role of words' semantics in sentiment analysis of microblogs, aiming mainly at addressing the above problem. In particular, Twitter is used as a case study of microblogging platforms to investigate whether capturing the sentiment of words with respect to their semantics leads to more accurate sentiment analysis models on Twitter. To this end, several approaches are proposed in this thesis for extracting and incorporating two types of word semantics for sentiment analysis: contextual semantics (i.e., semantics captured from words' co-occurrences) and conceptual semantics (i.e., semantics extracted from external knowledge sources). Experiments are conducted with both types of semantics by assessing their impact in three popular sentiment analysis tasks on Twitter; entity-level sentiment analysis, tweet-level sentiment analysis and context-sensitive sentiment lexicon adaptation. Evaluation under each sentiment analysis task includes several sentiment lexicons, and up to 9 Twitter datasets of different characteristics, as well as comparing against several state-of-the-art sentiment analysis approaches widely used in the literature. The findings from this body of work demonstrate the value of using semantics in sentiment analysis on Twitter. The proposed approaches, which consider words' semantics for sentiment analysis at both, entity and tweet levels, surpass non-semantic approaches in most datasets

From Associations to Info-Sociations: Civic associations and ICTs in Two Asian Cities

Non-profit civic associations are experimenting with information communications technologies (ICTs) in their work inside ‘global cities.’ The “info-sociational” concept is introduced in this paper as a heuristic and an approach for investigating ICT-linked organizational, participatory and spatial transformations in civic associations. The info-sociational approach is applied to four cases of civic environmental associations in two ‘Asian tiger’ cities-Hong Kong and Taipei-to compare their experiments with: urban map mash-ups; digital storytelling; participatory e-platforms; green new media; and networked activism. An info-sociational approach-besides providing a frame for comparatively analyzing digital practices amongst civic groups-arguably advances theory on the co-evolution of civic associations and ICTs.Special Issue: Linking the Local with the Global within Community Informatic

Mining, Modeling, and Analyzing Real-Time Social Trails

Real-time social systems are the fastest growing phenomena on the web, enabling millions of users to generate, share, and consume content on a massive scale. These systems are manifestations of a larger trend toward the global sharing of the real-time interests, affiliations, and activities of everyday users and demand new computational approaches for monitoring, analyzing, and distilling information from the prospective web of real-time content. In this dissertation research, we focus on the real-time social trails that reflect the digital footprints of crowds of real-time web users in response to real-world events or online phenomena. These digital footprints correspond to the artifacts strewn across the real-time web like posting of messages to Twitter or Facebook; the creation, sharing, and viewing of videos on websites like YouTube; and so on. While access to social trails could benefit many domains there is a significant research gap toward discovering, modeling, and leveraging these social trails. Hence, this dissertation research makes three contributions: • The first contribution of this dissertation research is a suite of efficient techniques for discovering non-trivial social trails from large-scale real-time social systems. We first develop a communication-based method using temporal graphs for discovering social trails on a stream of conversations from social messaging systems like instant messages, emails, Twitter directed or @ messages, SMS, etc. and then develop a content-based method using locality sensitive hashing for discovering content based social trails on a stream of text messages like Tweet stream, stream of Facebook messages, YouTube comments, etc. • The second contribution of this dissertation research is a framework for modeling and predicting the spatio-temporal dynamics of social trails. In particular, we develop a probabilistic model that synthesizes two conflicting hypotheses about the nature of online information spread: (i) the spatial influence model, which asserts that social trails propagates to locations that are close by; and (ii) the community affinity influence model, which asserts that social trail prop- agates between locations that are culturally connected, even if they are distant. • The third contribution of this dissertation research is a set of methods for social trail analytics and leveraging social trails for prognostic applications like real-time content recommendation, personalized advertising, and so on. We first analyze geo-spatial social trails of hashtags from Twitter, investigate their spatio-temporal dynamics and then use this analysis to develop a framework for recommending hashtags. Finally, we address the challenge of classifying social trails efficiently on real-time social systems

CURATION AND MANAGEMENT OF CULTURAL HERITAGE THROUGH LIBRARIES

Libraries, museums and archives hold valuable collections in a variety of media, presenting a vast body of knowledge rooted in the history of human civilisation. These form the repository of the wisdom of great works by thinkers of past and the present. The holdings of these institutions are priceless heritage of the mankind as they preserve documents, ideas, and the oral and written records. To value the cultural heritage and to care for it as a treasure bequeathed to us by our ancestors is the major responsibility of libraries. The past records constitute a natural resource and are indispensable to the present generation as well as to the generations to come. Libraries preserve the documentary heritage resources for which they are primarily responsible. Any loss of such materials is simply irreplaceable. Therefore, preserving this intellectual, cultural heritage becomes not only the academic commitment but also the moral responsibility of the librarians/information scientists, who are in charge of these repositories. The high quality of the papers and the discussion represent the thinking and experience of experts in their particular fields. The contributed papers also relate to the methodology used in libraries in Asia to provide access to manuscripts and cultural heritage. The volume discusses best practices in Knowledge preservation and how to collaborate and preserve the culture. The book also deals with manuscript and archives issues in the digital era. The approach of this book is concise, comprehensively, covering all major aspects of preservation and conservation through libraries. The readership of the book is not just limited to library and information science professionals, but also for those involved in conservation, preservation, restoration or other related disciplines. The book will be useful for librarians, archivists and conservators. We thank the Sunan Kalijaga University, Special Libraries Association- Asian Chapter for their trust and their constant support, all the contributors for their submissions, the members of the Local and International Committee for their reviewing effort for making this publication possible

The self-management of chronic conditions and the experience of cyber-victimisation in the United Kingdom

A thesis submitted to the University of Bedfordshire, in fulfilment of the requirements for the degree of Doctor of Philosophy (PhD)Background The victimisation of people with chronic conditions and disabilities has physical, mental and psychosocial consequences. Although this victimisation is documented, victims usually struggle to get the proper support. Research focusing on cyber-victimisation of people with long-term conditions is limited and lacks qualitative elements. Aims This study aimed to understand the impact of cyber-victimisation among individuals living with chronic conditions in the UK. Methods A mixed-method design was adopted using a mixed-method online survey, followed by in-depth interviews with victims. The participants were encouraged to share their voices as experts in their own experiences. This was supported by short interviews with general practitioners (GPs) as the gatekeepers to the health system and one of the supportive channels available to victims. A systematic review was completed and published, which helped to identify gaps in the literature. Primary data was collected from 55 victim support groups, patient-support groups, and via social media. The challenges in recruiting victims for this sensitive topic were identified and published to guide future research. The theoretical framework underpinning the study incorporated the Biographical Disruption model, Self-Management and Social Support. Results Quantitative data from 152 participants showed that almost one in every two people with chronic conditions was cyber-victimised (45.39%). In total, 76.81% of victims had a self-reported disability, and the relationship between cyber-victimisation and disability was statistically significant. Furthermore, 61.11% of victims reported that experiencing cyber-victimisation had affected their self-management plan. The highest impact was on lifestyle changes such as exercise, diet, avoiding triggers, and avoiding excessive smoking or alcohol drinking. This was followed by changes to medications and follow up with healthcare professionals. Indeed, 69% of victims perceived a worsened self-efficacy scale for health condition self-management following cyber-victimisation. In general, formal support was rated poor, with only 24.53% of victims having spoken to their GPs with variable responses. Six themes emerged from the qualitative data: Biomedical Events (overall health - physical complaints), Impact on Mental Health (psychological and psychiatric effects - helplessness), Multi-level Impact (existing vulnerability - disruption and reprioritisation), The Impact of Complexity (complex situation - struggle for support), Social Network Involvement (social isolation and victim blaming - controversial social support - misrepresentation of self), and Disability Discrimination (inclusion, culture and hate – tax and disability benefits). The participating GPs thought that cyber-victimisation had both mental and physical impact on people with chronic conditions, with concerns over online health forums use. GPs’ responses were influenced by individual variations. Conclusion Cyber-victimisation against people with chronic conditions is prevalent. It is a traumatic event that was introduced after the biographical disruption and working to cope with long-term conditions. It triggered significant fear, had a devastating impact and depleted victims of social support. The impact was multifaceted, and the results from the survey and interviews were convergent. They were also confirmed by the GPs’ input. The outcomes provided an in-depth understanding of the impact of cyber-victimisation on such marginalised groups. To initiate change, the results were summarised in a health promotion design that was informed by the participants and gatekeepers, and improved for dissemination. Further context-specific, condition-specific, participatory, and multidisciplinary work are indicated