SOCIAL MEDIA ANALYTICS − A UNIFYING DEFINITION, COMPREHENSIVE FRAMEWORK, AND ASSESSMENT OF ALGORITHMS FOR IDENTIFYING INFLUENCERS IN SOCIAL MEDIA

Given its relative infancy, there is a dearth of research on a comprehensive view of business social media analytics (SMA). This dissertation first examines current literature related to SMA and develops an integrated, unifying definition of business SMA, providing a nuanced starting point for future business SMA research. This dissertation identifies several benefits of business SMA, and elaborates on some of them, while presenting recent empirical evidence in support of foregoing observations. The dissertation also describes several challenges facing business SMA today, along with supporting evidence from the literature, some of which also offer mitigating solutions in particular contexts. The second part of this dissertation studies one SMA implication focusing on identifying social influencer. Growing social media usage, accompanied by explosive growth in SMA, has resulted in increasing interest in finding automated ways of discovering influencers in online social interactions. Beginning 2008, many variants of multiple basic approaches have been proposed. Yet, there is no comprehensive study investigating the relative efficacy of these methods in specific settings. This dissertation investigates and reports on the relative performance of multiple methods on Twitter datasets containing between them tens of thousands to hundreds of thousands of tweets. Accordingly, the second part of the dissertation helps further an understanding of business SMA and its many aspects, grounded in recent empirical work, and is a basis for further research and development. This dissertation provides a relatively comprehensive understanding of SMA and the implementation SMA in influencer identification

Verbal Aggressiveness Exploration through Complete Social Network Analysis: Using Physical Education Students’ Class as an Illustration

Aim of this study: is a) to detect informal structures (power and position in structures of targeting of verbal aggression) among higher education students, b) to analyze determinants of these structural properties, and c) to propose a typology of verbal aggression targets. Complete network analysis was applied on a sample of a 53 students in Physical Education Faculty, Thessaly University, Greece. Four network analysis algorithms were used: in-degree, Katz status, pagerank, authority. Non-network and network determinants of being target of verbal aggressiveness were analyzed. Gender (particularly femaleness), high grade of school graduation and parents education level protect from verbal aggressiveness while interest in post-graduate study seems to provoke criticism. Ignoring public opinion and intimacy with many “close friends” are positively correlated with verbal aggressiveness. Young (and not old-fashioned) appearance, imposing and eccentric appearance characteristics seem to protect against verbal aggressiveness. Big corporal size, dark skin colors seem to provoke insults in case of female students. Eminent economic state is also provocative as it implies pretentiousness. Phone verbal aggressiveness appears mainly among male students. Eminent qualities such as “good friend”, “desirable partner” etc make someone an eminent target for verbal aggressiveness. Verbal aggressiveness presents a catholic character and thus seems to be destructive and not constructive. Intellectual abilities (weaknesses) constitute a verbal aggressiveness target core. The following types of verbal aggressiveness targets are proposed: a) the “general ‘black sheep’”, b) the “contemptible type”, c) the “bagger” type, d) the “victim of mockers” and e) the “victim of serial criticizers”

Spreading semantic information by Word Sense Disambiguation

This paper presents an unsupervised approach to solve semantic ambiguity based on the integration of the Personalized PageRank algorithm with word-sense frequency information. Natural Language tasks such as Machine Translation or Recommender Systems are likely to be enriched by our approach, which includes semantic information that obtains the appropriate word-sense via support from two sources: a multidimensional network that includes a set of different resources (i.e. WordNet, WordNet Domains, WordNet Affect, SUMO and Semantic Classes); and the information provided by word-sense frequencies and word-sense collocation from the SemCor Corpus. Our series of results were analyzed and compared against the results of several renowned studies using SensEval-2, SensEval-3 and SemEval-2013 datasets. After conducting several experiments, our procedure produced the best results in the unsupervised procedure category taking SensEval campaigns rankings as reference.This research work has been partially funded by the University of Alicante, Generalitat Valenciana , Spanish Government, Ministerio de Educación, Cultura y Deporte and ASAP - Ayudas Fundación BBVA a equipos de investigación científica 2016(FUNDACIONBBVA2-16PREMIO) through the projects, TIN2015- 65100-R, TIN2015-65136-C2-2-R, PROMETEOII/2014/001, GRE16- 01: “Plataforma inteligente para recuperación, análisis y representación de la información generada por usuarios en Internet” and PR16_SOC_0013

DYNAMICS OF COLLABORATIVE NAVIGATION AND APPLYING DATA DRIVEN METHODS TO IMPROVE PEDESTRIAN NAVIGATION INSTRUCTIONS AT DECISION POINTS FOR PEOPLE OF VARYING SPATIAL APTITUDES

Cognitive Geography seeks to understand individual decision-making variations based on fundamental cognitive differences between people of varying spatial aptitudes. Understanding fundamental behavioral discrepancies among individuals is an important step to improve navigation algorithms and the overall travel experience. Contemporary navigation aids, although helpful in providing turn-by-turn directions, lack important capabilities to distinguish decision points for their features and importance. Existing systems lack the ability to generate landmark or decision point based instructions using real-time or crowd sourced data. Systems cannot customize personalized instructions for individuals based on inherent spatial ability, travel history, or situations. This dissertation presents a novel experimental setup to examine simultaneous wayfinding behavior for people of varying spatial abilities. This study reveals discrepancies in the information processing, landmark preference and spatial information communication among groups possessing differing abilities. Empirical data is used to validate computational salience techniques that endeavor to predict the difficulty of decision point use from the structure of the routes. Outlink score and outflux score, two meta-algorithms that derive secondary scores from existing metrics of network analysis, are explored. These two algorithms approximate human cognitive variation in navigation by analyzing neighboring and directional effect properties of decision point nodes within a routing network. The results are validated by a human wayfinding experiment, results show that these metrics generally improve the prediction of errors. In addition, a model of personalized weighting for users\u27 characteristics is derived from a SVMrank machine learning method. Such a system can effectively rank decision point difficulty based on user behavior and derive weighted models for navigators that reflect their individual tendencies. The weights reflect certain characteristics of groups. Such models can serve as personal travel profiles, and potentially be used to complement sense-of-direction surveys in classifying wayfinders. A prototype with augmented instructions for pedestrian navigation is created and tested, with particular focus on investigating how augmented instructions at particular decision points affect spatial learning. The results demonstrate that survey knowledge acquisition is improved for people with low spatial ability while decreased for people of high spatial ability. Finally, contributions are summarized, conclusions are provided, and future implications are discussed

Diversity of thought in the blogosphere: implications for influencing and monitoring image

A blog, a shortened form of weblog, is a website where an author shares thoughts in posts or entries. Most blogs permit readers to add comments to posts and thereby be a conversational mechanism. One way that companies have started to use blogs is to monitor their corporate image (in this dissertation, the term image is used in reference to corporate, brand and/or product image). This study focuses on how common socio-psychological processes mediate consumers’ revelation of corporate image in the blogosphere. Centering resonance analysis, a means of measuring similarity between two bodies of text, is used in conjunction with multidimensional scaling to locate text as cognitive objects in a space. Clusters are then detected and measured to quantify diversity in the thoughts expressed. Detected patterns are studied from a social process theory perspective, where complex phenomena are hypothesized to be the result of the interaction of simpler processes. A majority of blog commenters compromise the expression of their thoughts to gain social acceptance. This study identifies the most extreme of such people so companies who monitor blogs can assign less weight to image indications gained from them as they may be merely expressing thoughts that are intended to maintain social acceptance. It was also found that single-theme blogs attract a readership with similarly narrow interests. The boldest and most diverse thinkers among comment writers have the most impact because of their ability to provoke the thinking of others. However, commenters who repeat the same ideas have little effect, suggesting that introducing shills is unlikely to shift the sentiment of a blog’s readership. People participate in blog communities for reasons (e.g., need for community) that may undermine thought diversity. However, there may be value in serving those needs even though no valuable insights are provided into image or directions for product development. Members of homogeneous-thinking communities were observed to more actively participate, with greater longevity. This may increase loyalty to the company hosting the blog

CHORUS Deliverable 2.1: State of the Art on Multimedia Search Engines

Based on the information provided by European projects and national initiatives related to multimedia search as well as domains experts that participated in the CHORUS Think-thanks and workshops, this document reports on the state of the art related to multimedia content search from, a technical, and socio-economic perspective. The technical perspective includes an up to date view on content based indexing and retrieval technologies, multimedia search in the context of mobile devices and peer-to-peer networks, and an overview of current evaluation and benchmark inititiatives to measure the performance of multimedia search engines. From a socio-economic perspective we inventorize the impact and legal consequences of these technical advances and point out future directions of research

A Framework to Evaluate Information Quality in Public Administration Website

This paper presents a framework aimed at assessing the capacity of Public Administration bodies (PA) to offer a good quality of information and service on their web portals. Our framework is based on the extraction of “.it” domain names registered by Italian public institutions and the subsequent analysis of their relative websites. The analysis foresees an automatic gathering of the web pages of PA portals by means of web crawling and an assessment of the quality of their online information services. This assessment is carried out by verifying their compliance with current legislation on the basis of the criteria established in government guidelines[1]. This approach provides an ongoing monitoring process of the PA websites that can contribute to the improvement of their overall quality. Moreover, our approach can also hopefully be of benefit to local governments in other countries. Available at: https://aisel.aisnet.org/pajais/vol5/iss3/3