The Impact of Manipulation in Internet Stock Message Boards

Internet message boards are often used to spread information in order to manipulate financial markets. Although this hypothesis is supported by many cases reported in the literature and in the media, the real impact of manipulation in online forums on financial markets remains an open question. This paper is on the effect of manipulation in internet stock message boards on financial markets by employing a unique corpus of moderated messages to investigate market manipulation.Internet message board administrators use the process of moderation to restrict market manipulation. We find that manual supervision of stock message boards by moderators does not effectively protect Internet users against manipulation. By focusing on messages that have been moderated as manipulative due to ramping, we show ramping is positively related to market returns, volatility and volume. Stocks with higher turnover, lower price level, lower market capitalization and higher volatility are more common targets of ramping

Subjective Summaries: Summarizing Documents Using the Readers' Feedbacks

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A User-Centered Approach for Evaluating Query Expansion Methods

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Automatically Characterizing Salience Using Readers' Feedback

Salience is an important characteristic of information influencing users’ cognitive and emotional states. For example, salient parts of a document are those that readers will find moving or provoking. This article studies the salience concept and its meanings in linguistics and information retrieval. Then it analyses the main drawbacks of content-based techniques for automatic identification of salient passages in a document. A new context-based method for overcoming these difficulties is subsequently presented. Our method identifies passages that readers have reacted to by analyzing their textual feedback. Our experimentation with blog posts revealed that it is effective and can be on 90% of commented posts

Identifying Commented Passages of Documents Using Implicit Hyperlinks

International audienceThis paper addresses the issue of automatically selecting passages of blog posts using readers' comments. The problem is difficult because: (i) the textual content of blogs is often noisy, (ii) comments do not always target passages of the posts and, (iii) comments are not equally useful for identifying important passages. We have developed a system for selecting commented passages which takes as input blog posts and their comments and delivers, for each post, the sentences of the post which are the most commented and/or the most discussed. Our approach combines three steps to identify commented passages of a post. The first step is to remove the complexity of processing the contents of posts and comments using heuristics adapted to the language of the blog. The second step is to find useful comments and assigns them a degree of relevance using a model automatically built and validated by an expert. The third step is to identify important passages using relevant comments. We conducted two experiments to evaluate the usefulness and the effectiveness of our approach. The first study show that in only 50% of the posts, the most commented sentence elicited by our approach corresponds to the post extract generated using generic summarization. In the second study, human participants confirmed that, in practice, selected passages are frequently commented passages

CONQUERIES: An Agent that Supports Query Expansion

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Identification des Parties Saillantes des Textes Commentés

National audienceCet article étudie le concept de saillance et ses divers usages en linguistique. Il analyse les difficultés des techniques basées sur le contenu pour l'identification des passages saillants d'un document. Pour pallier ces difficultés, une nouvelle méthode basée sur l'utilisation du contexte est décrite. Cette méthode identifie les passages sur lesquels les lecteurs ont réagi en analysant leur commentaires. Une expérimentation prouve l'intérêt de la méthode et de son applicabilité

Hierarchical cluster visualization in web mapping systems

This paper presents a technique for visualizing large spatial data sets in Web Mapping Systems (WMS). The technique creates a hierarchical clustering tree, which is subsequently used to extract clusters that can be displayed at a given scale without cluttering the map. Voronoi polygons are used as aggregation symbols to represent the clusters. This technique retains hierarchical relationships between data items at different scales. In addition, aggregation symbols do not overlap, and their sizes and the number of points that they cover is controlled by the same parameter. A prototype has been implemented and tested showing the effectiveness of the method for visualizing large data sets in WMS.4 page(s