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

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

Improving evolutionary algorithms by MEANS of an adaptive parameter control approach

Evolutionary algorithms (EA) constitute a class of optimization methods that is widely used to solve complex scientific problems. However, EA often converge prematurely over suboptimal solutions, the evolution process is computational expensive, and setting the required EA parameters is quite difficult. We believe that the best way to address these problems is to begin by improving the parameter setting strategy, which will in turn improve the search path of the optimizer, and, we hope, ultimately help prevent premature convergence and relieve the computational burden. The strategy that will achieve this outcome, and the one we adopt in this research, is to ensure that the parameter setting approach takes into account the search path and attempts to drive it in the most advantageous direction. Our objective is therefore to develop an adaptive parameter setting approach capable of controlling all the EA parameters at once. To interpret the search path, we propose to incorporate the concept of exploration and exploitation into the feedback indicator. The first step is to review and study the available genotypic diversity measurements used to characterize the exploration of the optimizer over the search space. We do this by implementing a specifically designed benchmark, and propose three diversity requirements for evaluating the meaningfulness of those measures as population diversity estimators. Results show that none of the published formulations is, in fact, a qualified diversity descriptor. To remedy this, we introduce a new genotypic formulation here, the performance analysis of which shows that it produces better results overall, notwithstanding some serious defects. We initiate a similar study aimed at describing the role of exploitation in the search process, which is to indicate promising regions. However, since exploitation is mainly driven by the individuals’ fitness, we turn our attention toward phenotypic convergence measures. Again, the in-depth analysis reveals that none of the published phenotypic descriptors is capable of portraying the fitness distribution of a population. Consequently, a new phenotypic formulation is developed here, which shows perfect agreement with the expected population behavior. On the strength of these achievements, we devise an optimizer diagnostic tool based on the new genotypic and phenotypic formulations, and illustrate its value by comparing the impacts of various EA parameters. Although the main purpose of this development is to explore the relevance of using both a genotypic and a phenotypic measure to characterize the search process, our diagnostic tool proves to be one of the few tools available to practitioners for interpreting and customizing the way in which optimizers work over real-world problems. With the knowledge gained in our research, the objective of this thesis is finally met, with the proposal of a new adaptive parameter control approach. The system is based on a Bayesian network that enables all the EA parameters to be considered at once. To the authors’ knowledge, this is the first parameter setting proposal devised to do so. The genotypic and phenotypic measures developed are combined in the form of a credit assignment scheme for rewarding parameters by, among other things, promoting maximization of both exploration and exploitation. The proposed adaptive system is evaluated over a recognized benchmark (CEC’05) through the use of a steady-state genetic algorithm (SSGA), and then compared with seven other approaches, like FAUC-RMAB and G-CMA-ES, which are state-of-the-art adaptive methods. Overall, the results demonstrate statistically that the new proposal not only performs as well as G-CMA-ES, but outperforms almost all the other adaptive systems. Nonetheless, this investigation revealed that none of the methods tested is able to locate global optimum over complex multimodal problems. This led us to conclude that synergy and complementarity among the parameters involved is probably missing. Consequently, more research on these topics is advised, with a view to devising enhanced optimizers. We provide numerous recommendations for such research at the end of this thesis