Search results

The purpose of this paper is primarily to report on a 2011 online discussion on tablets and their adoption in libraries, as observed by the researcher in blog postings and micro‐blog postings.

The researcher examined blogs and tweets about the diffusion of tablets in academic libraries to find out why early adopters or academic librarians adopted tablets and implemented them into library services.

Results reveal that academic librarians and libraries adopt and integrate tablets into library services because they can offer wireless access to the library's e‐collection and e‐resources in ways better than e‐readers or smartphones and because librarians have some level of familiarity with using tablets for their own work purposes before they considered extending such purposes to users.

Academic libraries are investing in devices to facilitate users' access to growing e‐resources. Tablet devices are one such option. However, many tablets are expensive, equalling or totalling more than the costs of laptops. The decision to adopt and implement them into library services needs to be informed by the experiences of others, in order to determine if it is a worthwhile purchase.

This paper departs from the general pattern of library literature on the subject of tablet adoption, by breaking with the tradition of being only informed by practice and emerging trial and error, to a more reflective approach to those experiences informed by Rogers' theory of the diffusions of innovations.

The purpose of this exploratory research paper is twofold. First, to identify how wine is being talked about within the Twitter environment and second to examine whether the constructs of soft and hard value can be demonstrated within the social media, Twitter content. The overall aim is to establish whether Twitter can create value for wine stakeholders.

A netnographic approach has been adopted through the collection of 1,500 English language tweets on the subject of wine which were analysed using Nvivo qualitative data analysis software against the constructs of value found in the literature.

The early findings suggest that Twitter can create soft value for wine focused businesses but that hard value is not yet strongly evident. Wine marketers need to be more aware of both the content of wine tweets and the profiles of those who tweet.

The use of social media within wine businesses is in its infancy. Wine focused businesses are currently not optimising value creation through Twitter. Whilst Twitter does exhibit value, wine marketers, wine brands and wine retailers are not engaging with consumers who are talking about wine via Twitter.

This paper has provided evidence that there is value within social media, specifically Twitter. The contribution of this paper lies in identifying not only the types of value that wine businesses could develop by embracing Twitter but also how the different profiles use Twitter differently.

An essential part of the transfer of knowledge in the tourism and hospitality industry, destination image is defined as the expression of objective knowledge, imagination, and the subjective emotions of the tourist. Social media is profoundly changing the way the tourist images and interacts with the destination environment. In turn, firms in the industry are seeking to leverage the power of social media to gain insights into tourist cognition and behavior. In this chapter, we analyze various social media to investigate knowledge transfer relating to two groups of hotels in Philadelphia, and we propose a methodology to predict future lodging demand from empirical data in line with the objectives of the t-Forum.

A key concept within the wider practice of crisis informatics is situational awareness, which refers to the way that social media can be used to provide local, situation-specific information. This information may be used for disaster response communication and resilience building. The purpose of this study is to argue that, in this context, situational awareness relies on an overly literal interpretation of information sent via the micro-blogging service Twitter.

The paper reviews relevant literature, including work that seeks to establish social media as a key tool in terms of crisis informatics. It discusses relevant theoretical debates and uses recent examples to interrogate these concepts.

The paper identifies several issues that complicate the interpretation and application of information from Twitter during crises. Not only can misinformation circulate during disaster events, the relationship between complex meaning-making processes, taking place both online and offline, is not yet well-understood. Locating digital data in physical time and space can better illuminate the dynamics of communication on Twitter.

This is a theoretical discussion paper. It requires empirical work to question and to develop its theoretical findings.

The paper discusses implications for designing crisis informatics tools. It suggests that the cross-referencing of data may assist in the verification and interpretation of tweet content in regards to situational awareness.

This paper advances an important theoretical discussion with practical implications in an emergent field in crisis communication. It highlights the importance of focusing on temporal and spatial parameters for better evaluating situational awareness.

Social websites have become a major medium for social interaction. From Facebook to MySpace to emergent sites like Twitter, social websites are increasing exponentially in user numbers and unique visits every day. How do these websites encourage sociability? What features or design practices enable users to socialize with other users? The purpose of this paper is to explore sociability on the social web and details how different social websites encourage their users to interact.

Four social websites (Facebook, MySpace, LinkedIn and Twitter) were examined from a user study perspective. After thoroughly participating on the websites, a series of observations were recorded from each experience. These experiences were then compared to understand the different approaches of each website.

Social websites use a number of different approaches to encourage sociability amongst their users. Facebook promotes privacy and representing “real world” networks in web environment, while MySpace promotes publicity and representing both real world and virtual networks in a web environment. Niche websites like LinkedIn and Twitter focus on more specific aspects of community and technology, respectively.

A comparison of different models of sociability does not yet exist. This study focuses specifically on what makes social websites “social.”

Symmetric communication and relationship building are core principles of public relations, which have been highlighted for CSR communication. The purpose of this paper is to develop three different communication strategies for CSR communication in Twitter, of which each contributes differently to the ideals of symmetric communication and relationship building. The framework is then applied to analyze how companies use the micro-blogging service Twitter for CSR communication.

Social network analysis is used to identify the 30 most central corporate accounts in a CSR Twitter network.

From the social network analysis 40,000 tweets are extracted and manually coded. Anova is applied to investigate differences in the weighting of CSR topics between the different strategies.

So far not much is known about how social media, such as Twitter, contribute to the core principles of public relations, if companies use social media to foster symmetric communication and relationship management, or which CSR topics they address.

As a new communication paradigm, social networking sites (SNS) have boosted information diffusion and viral marketing. Prior researchers have identified various factors affecting information dissemination on SNS. However, they often focus on limited factors and there is a lack of an integrated theoretical framework that explains aspects of relevant factors. Besides, the research on the impacts of relationships on individual retweeting behavior is still controversial. The purpose of this paper is to propose a theoretical framework to systematically investigate the determinants of individual dissemination behavior on SNS based on the elaboration likelihood model (ELM). Moreover, the authors also examine the relative importance of those relevant factors.

The authors randomly selected 1,250 members of Twitter and crawled posts published by each member since he/she created the Twitter account using Twitter API. The authors processed the data to create panel data and tested hypotheses with the panel logit model.

Factors both on the central route and on the peripheral route of ELM have positive impacts on individual dissemination behavior. Among them, information receiver-related factor and relationships-related factors are the most influential. Contrastingly, source-related factors are the least influential. Furthermore, the authors find that social tie strength mediates almost 50 percent of the effect of value homophily on individual dissemination behavior.

The authors are the first to directly apply ELM to examine individual dissemination behavior on SNS. By integrating factors into the two information processing routes, They incorporate relevant factors into the model and systematically analyze their impacts on individual retweeting behavior on SNS. The research offers at least one explanation for the contradictory findings about the effect of homophily on individual sharing behavior in previous research. The authors propose new variables that gauge topical relevance and interpersonal value homophily on SNS.

Universities are populated with a wide range of disciplines. The science disciplines and their instructors are stereotyped as tech-savvy while in the past humanities faculty have sometimes been seen as technophobic and traditional. As we advance through the second decade of the 21st century, we find instructors in all areas are embracing new technologies in their teaching. Our students have been born digital (Tapscott, 2009) and have not only experienced online games and social networking technologies such as Facebook but thrive in them. It should not be surprising that many of our colleagues are trying out the use of social media in their courses. This volume embodies a sharing of such experiences with the aim of moving you up the learning curve so that your thinking about how these new technologies might spark excitement, interaction, sharing, and enhanced work and learning by your students.

The purpose of this study is to facilitate the task of finding appropriate information to read about, and searching for people who are in the same field of interest. Knowing that more people keep up with new streaming information on Twitter micro-blogging service. With the immense number of micro-posts shared via the follower/followee network graph, Twitter users find themselves in front of millions of tweets, which makes the task crucial.

In this paper, a long short–term memory (LSTM) model that relies on the latent Dirichlet allocation (LDA) output vector for followee recommendation, the LDA model applied as a topic modeling strategy is proposed.

This study trains the model using a real-life data set extracted based on Twitter follower/followee architecture. It confirms the effectiveness and scalability of the proposed approach. The approach improves the state-of-the-art models average-LSTM and time-LSTM.

This study improves mainly the existing followee recommendation systems. Because, unlike previous studies, it applied a non-hand-crafted method which is the LSTM neural network with LDA model for topics extraction. The main limitation of this study is the cold-start users cannot be treated, also some active fake accounts may not be detected.

The aim of this approach is to assist users seeking appropriate information to read about, by choosing appropriate profiles to follow.

This approach consolidates the social relationship between users in a microblogging platform by suggesting like-minded people to each other. Thus, finding users with the same interests will be easy without spending a lot of time seeking relevant users.

Instead of classic recommendation models, the paper provides an efficient neural network searching method to make it easier to find appropriate users to follow. Therefore, affording an effective followee recommendation system.

Hate speech in recent times has become a troubling development. It has different meanings to different people in different cultures. The anonymity and ubiquity of the social media provides a breeding ground for hate speech and makes combating it seems like a lost battle. However, what may constitute a hate speech in a cultural or religious neutral society may not be perceived as such in a polarized multi-cultural and multi-religious society like Nigeria. Defining hate speech, therefore, may be contextual. Hate speech in Nigeria may be perceived along ethnic, religious and political boundaries. The purpose of this paper is to check for the presence of hate speech in social media platforms like Twitter, and to what degree is hate speech permissible, if available? It also intends to find out what monitoring mechanisms the social media platforms like Facebook and Twitter have put in place to combat hate speech. Lexalytics is a term coined by the authors from the words lexical analytics for the purpose of opinion mining unstructured texts like tweets.

This research developed a Python software called polarized opinions sentiment analyzer (POSA), adopting an ego social network analytics technique in which an individual’s behavior is mined and described. POSA uses a customized Python N-Gram dictionary of local context-based terms that may be considered as hate terms. It then applied the Twitter API to stream tweets from popular and trending Nigerian Twitter handles in politics, ethnicity, religion, social activism, racism, etc., and filtered the tweets against the custom dictionary using unsupervised classification of the texts as either positive or negative sentiments. The outcome is visualized using tables, pie charts and word clouds. A similar implementation was also carried out using R-Studio codes and both results are compared and a t-test was applied to determine if there was a significant difference in the results. The research methodology can be classified as both qualitative and quantitative. Qualitative in terms of data classification, and quantitative in terms of being able to identify the results as either negative or positive from the computation of text to vector.

The findings from two sets of experiments on POSA and R are as follows: in the first experiment, the POSA software found that the Twitter handles analyzed contained between 33 and 55 percent hate contents, while the R results show hate contents ranging from 38 to 62 percent. Performing a t-test on both positive and negative scores for both POSA and R-studio, results reveal p-values of 0.389 and 0.289, respectively, on an α value of 0.05, implying that there is no significant difference in the results from POSA and R. During the second experiment performed on 11 local handles with 1,207 tweets, the authors deduce as follows: that the percentage of hate contents classified by POSA is 40 percent, while the percentage of hate contents classified by R is 51 percent. That the accuracy of hate speech classification predicted by POSA is 87 percent, while free speech is 86 percent. And the accuracy of hate speech classification predicted by R is 65 percent, while free speech is 74 percent. This study reveals that neither Twitter nor Facebook has an automated monitoring system for hate speech, and no benchmark is set to decide the level of hate contents allowed in a text. The monitoring is rather done by humans whose assessment is usually subjective and sometimes inconsistent.

This study establishes the fact that hate speech is on the increase on social media. It also shows that hate mongers can actually be pinned down, with the contents of their messages. The POSA system can be used as a plug-in by Twitter to detect and stop hate speech on its platform. The study was limited to public Twitter handles only. N-grams are effective features for word-sense disambiguation, but when using N-grams, the feature vector could take on enormous proportions and in turn increasing sparsity of the feature vectors.

The findings of this study show that if urgent measures are not taken to combat hate speech there could be dare consequences, especially in highly polarized societies that are always heated up along religious and ethnic sentiments. On daily basis tempers are flaring in the social media over comments made by participants. This study has also demonstrated that it is possible to implement a technology that can track and terminate hate speech in a micro-blog like Twitter. This can also be extended to other social media platforms.

This study will help to promote a more positive society, ensuring the social media is positively utilized to the benefit of mankind.

The findings can be used by social media companies to monitor user behaviors, and pin hate crimes to specific persons. Governments and law enforcement bodies can also use the POSA application to track down hate peddlers.