The Exploration and Development of Alternative Management Strategies for Mining and Recreational Activities Atop Mount Antero and Mount White; Chaffee County, Colorado

The Mount Antero/White area is a popular prospecting area. Recent expansions in the recreation economy is drawing more visitors to the area. Consequently, visitors may be placing unsustainable pressures on the landscape. In order to help rectify this, the legal, ecological, geologic, aesthetic, recreational, historic, social, and economic character of the Antero/White area has been identified. Four feasible management alternatives have also been recognized. They are a) take no new management actions, b) prohibit motorized activities in the area, c) develop a mineralogical park, and d) a combination of options b and c. Option C has been defended, as it best balances the desires of area users with the underlying ecological and geological character of the area.

Towards a unified framework for opinion retrieval, mining and summarization

The exponential increase of subjective, user-generated content since the birth of the Social Web, has led to the necessity of developing automatic text processing systems able to extract, process and present relevant knowledge. In this paper, we tackle the Opinion Retrieval, Mining and Summarization task, by proposing a unified framework, composed of three crucial components (information retrieval, opinion mining and text summarization) that allow the retrieval, classification and summarization of subjective information. An extensive analysis is conducted, where different configurations of the framework are suggested and analyzed, in order to determine which is the best one, and under which conditions. The evaluation carried out and the results obtained show the appropriateness of the individual components, as well as the framework as a whole. By achieving an improvement over 10% compared to the state-of-the-art approaches in the context of blogs, we can conclude that subjective text can be efficiently dealt with by means of our proposed framework.

TrendMiner: Large-scale Cross-lingual Trend Mining Summarization of Real-time Media Streams

El reciente crecimiento masivo de medios on-line y el incremento de los contenidos generados por los usuarios (por ejemplo, weblogs, Twitter, Facebook) plantea retos en el acceso e interpretación de datos multilingües de manera eficiente, rápida y asequible. El objetivo del proyecto TredMiner es desarrollar métodos innovadores, portables, de código abierto y que funcionen en tiempo real para generación de resúmenes y minería cross-lingüe de medios sociales a gran escala. Los resultados se están validando en tres casos de uso: soporte a la decisión en el dominio financiero (con analistas, empresarios, reguladores y economistas), monitorización y análisis político (con periodistas, economistas y políticos) y monitorización de medios sociales sobre salud con el fin de detectar información sobre efectos adversos a medicamentos.

Social Rankings: análisis visual de sentimientos en redes sociales

Social Rankings es una aplicación web que realiza un seguimiento en tiempo real de entidades en las redes sociales. Detecta y analiza las opiniones sobre estas entidades utilizando técnicas de análisis de sentimientos para generar un informe visual de su valoración y su evolución en el tiempo.

Harnessing Natural Resources for Development: New Legal Regimes for Localized Benefit Sharing in the Mining Sector

Thesis (Ph.D.)--University of Washington, 2016-06

Feature analysis of spammers in social networks with active honeypots:a case study of Chinese microblogging networks

In this poster we presented our preliminary work on the study of spammer detection and analysis with 50 active honeypot profiles implemented on Weibo.com and QQ.com microblogging networks. We picked out spammers from legitimate users by manually checking every captured user's microblogs content. We built a spammer dataset for each social network community using these spammer accounts and a legitimate user dataset as well. We analyzed several features of the two user classes and made a comparison on these features, which were found to be useful to distinguish spammers from legitimate users. The followings are several initial observations from our analysis on the features of spammers captured on Weibo.com and QQ.com. ¦The following/follower ratio of spammers is usually higher than legitimate users. They tend to follow a large amount of users in order to gain popularity but always have relatively few followers. ¦There exists a big gap between the average numbers of microblogs posted per day from these two classes. On Weibo.com, spammers post quite a lot microblogs every day, which is much more than legitimate users do; while on QQ.com spammers post far less microblogs than legitimate users. This is mainly due to the different strategies taken by spammers on these two platforms. ¦More spammers choose a cautious spam posting pattern. They mix spam microblogs with ordinary ones so that they can avoid the anti-spam mechanisms taken by the service providers. ¦Aggressive spammers are more likely to be detected so they tend to have a shorter life while cautious spammers can live much longer and have a deeper influence on the network. The latter kind of spammers may become the trend of social network spammer. © 2012 IEEE.

Timeline generation with social attention

Timeline generation is an important research task which can help users to have a quick understanding of the overall evolution of any given topic. It thus attracts much attention from research communities in recent years. Nevertheless, existing work on timeline generation often ignores an important factor, the attention attracted to topics of interest (hereafter termed "social attention"). Without taking into consideration social attention, the generated timelines may not reflect users' collective interests. In this paper, we study how to incorporate social attention in the generation of timeline summaries. In particular, for a given topic, we capture social attention by learning users' collective interests in the form of word distributions from Twitter, which are subsequently incorporated into a unified framework for timeline summary generation. We construct four evaluation sets over six diverse topics. We demonstrate that our proposed approach is able to generate both informative and interesting timelines. Our work sheds light on the feasibility of incorporating social attention into traditional text mining tasks. Copyright © 2013 ACM.

A computational approach to measuring the correlation between expertise and social media influence for celebrities on microblogs

Existing approaches of social influence analysis usually focus on how to develop effective algorithms to quantize users' influence scores. They rarely consider a person's expertise levels which are arguably important to influence measures. In this paper, we propose a computational approach to measuring the correlation between expertise and social media influence, and we take a new perspective to understand social media influence by incorporating expertise into influence analysis. We carefully constructed a large dataset of 13,684 Chinese celebrities from Sina Weibo (literally 'Sina microblogging'). We found that there is a strong correlation between expertise levels and social media influence scores. In addition, different expertise levels showed influence variation patterns: high-expertise celebrities have stronger influence on the 'audience' in their expertise domains.

A Taxonomy of Big Data for Optimal Predictive Machine Learning and Data Mining

Big data comes in various ways, types, shapes, forms and sizes. Indeed, almost all areas of science, technology, medicine, public health, economics, business, linguistics and social science are bombarded by ever increasing flows of data begging to be analyzed efficiently and effectively. In this paper, we propose a rough idea of a possible taxonomy of big data, along with some of the most commonly used tools for handling each particular category of bigness. The dimensionality p of the input space and the sample size n are usually the main ingredients in the characterization of data bigness. The specific statistical machine learning technique used to handle a particular big data set will depend on which category it falls in within the bigness taxonomy. Large p small n data sets for instance require a different set of tools from the large n small p variety. Among other tools, we discuss Preprocessing, Standardization, Imputation, Projection, Regularization, Penalization, Compression, Reduction, Selection, Kernelization, Hybridization, Parallelization, Aggregation, Randomization, Replication, Sequentialization. Indeed, it is important to emphasize right away that the so-called no free lunch theorem applies here, in the sense that there is no universally superior method that outperforms all other methods on all categories of bigness. It is also important to stress the fact that simplicity in the sense of Ockham’s razor non-plurality principle of parsimony tends to reign supreme when it comes to massive data. We conclude with a comparison of the predictive performance of some of the most commonly used methods on a few data sets.

Social media and sentiment in bioenergy consultation

he push to widen participation in public consultation suggests social media as an additional mechanism through which to engage the public. Bioenergy companies need to build their capacity to communicate in these new media and to monitor the attitudes of the public and opposition organisations towards energy development projects. Design/methodology/approach This short paper outlines the planning issues bioenergy developments face and the main methods of communication used in the public consultation process in the UK. The potential role of social media in communication with stakeholders is identified. The capacity of sentiment analysis to mine opinions from social media is summarised, and illustrated using a sample of tweets containing the term ‘bioenergy’ Findings Social media have the potential to improve information flows between stakeholders and developers. Sentiment analysis is a viable Purpose The push to widen participation in public consultation suggests social media as an additional mechanism through which to engage the public. Bioenergy companies need to build their capacity to communicate in these new media and to monitor the attitudes of the public and opposition organisations towards energy development projects. Design/methodology/approach This short paper outlines the planning issues bioenergy developments face and the main methods of communication used in the public consultation process in the UK. The potential role of social media in communication with stakeholders is identified. The capacity of sentiment analysis to mine opinions from social media is summarised, and illustrated using a sample of tweets containing the term ‘bioenergy’ Findings Social media have the potential to improve information flows between stakeholders and developers. Sentiment analysis is a viable methodology, which bioenergy companies should be using to measure public opinion in the consultation process. Preliminary analysis shows promising results. Research limitations/implications Analysis is preliminary and based on a small dataset. It is intended only to illustrate the potential of sentiment analysis and not to draw general conclusions about the bioenergy sector. Originality/value Opinion mining, though established in marketing and political analysis, is not yet systematically applied as a planning consultation tool. This is a missed opportunity.

Mining in Peru: Indigenous and Peasant Communities vs. The State and Mining Capital

The Peruvian economy depends for its growth on the export of natural resources and investment in the mining and hydrocarbon sectors. Peruvian governments and mining corporations have confronted anti-mining protests in different ways. While the current government has introduced policies of social inclusion to soften the negative effects of the operations of mining capital and policies of dialogue to engage social actors with the essence of governmental policies, mining companies use corporate social responsibility programs as a cover for the devastating effects of their operations on the environment and the livelihoods and habitats of the indigenous and peasant communities. Curiously, in the current context of the declining commodity prices and export volumes the Peruvian government strengthens its extractivist model of development. This article argues that whatever government that follows the rules of capital cannot but favor the corporations. It points out the main adversaries of the indigenous and peasant communities and the problems to transform the locally and/or regionally struggle into a nationwide battle for another development model.

Text Analytics of Social Media: Sentiment Analysis, Event Detection and Summarization

In the last decade, large numbers of social media services have emerged and been widely used in people's daily life as important information sharing and acquisition tools. With a substantial amount of user-contributed text data on social media, it becomes a necessity to develop methods and tools for text analysis for this emerging data, in order to better utilize it to deliver meaningful information to users. Previous work on text analytics in last several decades is mainly focused on traditional types of text like emails, news and academic literatures, and several critical issues to text data on social media have not been well explored: 1) how to detect sentiment from text on social media; 2) how to make use of social media's real-time nature; 3) how to address information overload for flexible information needs. In this dissertation, we focus on these three problems. First, to detect sentiment of text on social media, we propose a non-negative matrix tri-factorization (tri-NMF) based dual active supervision method to minimize human labeling efforts for the new type of data. Second, to make use of social media's real-time nature, we propose approaches to detect events from text streams on social media. Third, to address information overload for flexible information needs, we propose two summarization framework, dominating set based summarization framework and learning-to-rank based summarization framework. The dominating set based summarization framework can be applied for different types of summarization problems, while the learning-to-rank based summarization framework helps utilize the existing training data to guild the new summarization tasks. In addition, we integrate these techneques in an application study of event summarization for sports games as an example of how to better utilize social media data.

Contrived Dereliction: Employing an aesthetic of decay at mining heritage sites

Conventional descriptions place conservation activities between the two poles of active restorative intervention and passive abandonment. This paper proposes that site stewards at mining heritage sites often follow presentation strategies that sit outside this neat dualism. Drawing on material presented in the form of three case studies, this paper identifies the actions these strategies entail and considers the results in terms of an aesthetic of decay. To consolidate the argument, a new overarching term is introduced to describe this strategy: contrived dereliction, in order to foreground its essential features. The paper then outlines the advantages, limitations and requirements of contrived dereliction as a heritage management and presentation practice.

Exploring the feasibility of applying data mining for library reference service improvement : a case study of Turku Main Library

Data mining, as a heatedly discussed term, has been studied in various fields. Its possibilities in refining the decision-making process, realizing potential patterns and creating valuable knowledge have won attention of scholars and practitioners. However, there are less studies intending to combine data mining and libraries where data generation occurs all the time. Therefore, this thesis plans to fill such a gap. Meanwhile, potential opportunities created by data mining are explored to enhance one of the most important elements of libraries: reference service. In order to thoroughly demonstrate the feasibility and applicability of data mining, literature is reviewed to establish a critical understanding of data mining in libraries and attain the current status of library reference service. The result of the literature review indicates that free online data resources other than data generated on social media are rarely considered to be applied in current library data mining mandates. Therefore, the result of the literature review motivates the presented study to utilize online free resources. Furthermore, the natural match between data mining and libraries is established. The natural match is explained by emphasizing the data richness reality and considering data mining as one kind of knowledge, an easy choice for libraries, and a wise method to overcome reference service challenges. The natural match, especially the aspect that data mining could be helpful for library reference service, lays the main theoretical foundation for the empirical work in this study. Turku Main Library was selected as the case to answer the research question: whether data mining is feasible and applicable for reference service improvement. In this case, the daily visit from 2009 to 2015 in Turku Main Library is considered as the resource for data mining. In addition, corresponding weather conditions are collected from Weather Underground, which is totally free online. Before officially being analyzed, the collected dataset is cleansed and preprocessed in order to ensure the quality of data mining. Multiple regression analysis is employed to mine the final dataset. Hourly visits are the independent variable and weather conditions, Discomfort Index and seven days in a week are dependent variables. In the end, four models in different seasons are established to predict visiting situations in each season. Patterns are realized in different seasons and implications are created based on the discovered patterns. In addition, library-climate points are generated by a clustering method, which simplifies the process for librarians using weather data to forecast library visiting situation. Then the data mining result is interpreted from the perspective of improving reference service. After this data mining work, the result of the case study is presented to librarians so as to collect professional opinions regarding the possibility of employing data mining to improve reference services. In the end, positive opinions are collected, which implies that it is feasible to utilizing data mining as a tool to enhance library reference service.

Automated Discovery of Social Networks in Online Learning Communities

As a way to gain greater insights into the operation of online communities, this dissertation applies automated text mining techniques to text-based communication to identify, describe and evaluate underlying social networks among online community members. The main thrust of the study is to automate the discovery of social ties that form between community members, using only the digital footprints left behind in their online forum postings. Currently, one of the most common but time consuming methods for discovering social ties between people is to ask questions about their perceived social ties. However, such a survey is difficult to collect due to the high investment in time associated with data collection and the sensitive nature of the types of questions that may be asked. To overcome these limitations, the dissertation presents a new, content-based method for automated discovery of social networks from threaded discussions, referred to as ‘name network’. As a case study, the proposed automated method is evaluated in the context of online learning communities. The results suggest that the proposed ‘name network’ method for collecting social network data is a viable alternative to costly and time-consuming collection of users’ data using surveys. The study also demonstrates how social networks produced by the ‘name network’ method can be used to study online classes and to look for evidence of collaborative learning in online learning communities. For example, educators can use name networks as a real time diagnostic tool to identify students who might need additional help or students who may provide such help to others. Future research will evaluate the usefulness of the ‘name network’ method in other types of online communities.