Engineering topics

Bell, Eric B

Publications and source records attributed to Bell, Eric B.

6 records

DOE OSTI2014

Automatic identification of abstract online groups

Online abstract groups, in which members aren't explicitly connected, can be automatically identified by computer-implemented methods. The methods involve harvesting records from social media and extracting content-based and structure-based features from each record. Each record includes a social-media posting and is associated with one or more entities. Each feature is stored on a data storage device and includes a computer-readable representation of an attribute of one or more records. The methods further involve grouping records into record groups according to the features of each record. Further still the methods involve calculating an n-dimensional surface representing each record group and defining an outlier as a record having feature-based distances measured from every n-dimensional surface that exceed a threshold value. Each of the n-dimensional surfaces is described by a footprint that characterizes the respective record group as an online abstract group.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2012

Identifying Differences in Cultural Behavior in Online Groups

We have developed methods to identify online communities, or groups, using a combination of structural information variables and content information variables from weblog posts and their comments to build a characteristic footprint for groups. We have worked with both explicitly connected groups and 'abstract' groups, in which the connection between individuals is in interest (as determined by content based features) and behavior (metadata based features) as opposed to explicit links. We find that these variables do a good job at identifying groups, placing members within a group, and helping determine the appropriate granularity for group boundaries. The group footprint can then be used to identify differences between the online groups. In the work described here we are interested in determining how an individual's online behavior is influenced by their membership in more than one group. For example, individuals belong to a certain culture; they may belong as well to a demographic group, and other 'chosen' groups such as churches or clubs. There is a plethora of evidence surrounding the culturally sensitive adoption, use, and behavior on the Internet. In this work we begin to investigate how culturally defined internet behaviors may influence behaviors of subgroups. We do this through a series of experiments in which we analyze the interaction between culturally defined behaviors and the behaviors of the subgroups. Our goal is to (a) identify if our features can capture cultural distinctions in internet use, and (b) determine what kinds of interaction there are between levels and types of groups.

99 GENERAL AND MISCELLANEOUS↗

DOE OSTI2012

Semantic Features for Classifying Referring Search Terms

When an internet user clicks on a result in a search engine, a request is submitted to the destination web server that includes a referrer field containing the search terms given by the user. Using this information, website owners can analyze the search terms leading to their websites to better understand their visitors needs. This work explores some of the features that can be used for classification-based analysis of such referring search terms. We present initial results for the example task of classifying HTTP requests countries of origin. A system that can accurately predict the country of origin from query text may be a valuable complement to IP lookup methods which are susceptible to the obfuscation of dereferrers or proxies. We suggest that the addition of semantic features improves classifier performance in this example application. We begin by looking at related work and presenting our approach. After describing initial experiments and results, we discuss paths forward for this work.

99 GENERAL AND MISCELLANEOUS↗

DOE OSTI2012

IDENTIFYING ON-LINE GROUPS BASED ON CONTENT AND COLLECTIVE BEHAVIORAL PATTERNS

Online communities, or groups, have largely been defined based on links, page rank, and eigenvalues. In this paper we explore identifying abstract groups, groups where member's interests and online footprints are similar but they are not necessarily connected to one another explicitly. We use a combination of structural information and content information from posts and their comments to build a footprint for groups. We find that these variables do a good job at identifying groups, placing members within a group, and help determine the appropriate granularity for group boundaries.

99 GENERAL AND MISCELLANEOUS↗

DOE OSTI2011

Automatically Identifying Groups Based on Content and Collective Behavioral Patterns of Group Members

99 GENERAL AND MISCELLANEOUS↗

DOE OSTI2010

Collaborative Knowledge Discovery & Marshalling for Intelligence & Security Applications

This paper discusses the Knowledge Encapsulation Framework, a flexible, extensible evidence-marshalling environment built upon a natural language processing pipeline and exposed to users via an open-source semantic wiki. We focus our discussion on applications of the framework to intelligence and security applications, specifically, an instantiation of the KEF environment for researching illicit trafficking in nuclear materials.

98 NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL P↗