Reihane Boghrati

TACIT: An open-source text analysis, crawling, and interpretation tool.

As human activity and interaction increasingly take place online, the digital residues of these activities provide a valuable window into a range of psychological and social processes. A great deal of progress has been made toward utilizing these opportunities; however, the complexity of managing and analyzing the quantities of data currently available has limited both the types of analysis used and the number of researchers able to make use of these data. Although fields such as computer science have developed a range of techniques and methods for handling these difficulties, making use of those tools has often required specialized knowledge and programming experience. The Text Analysis, Crawling, and Interpretation Tool (TACIT) is designed to bridge this gap by providing an intuitive tool and interface for making use of state-of-the-art methods in text analysis and large-scale data management. Furthermore, TACIT is implemented as an open, extensible, plugin-driven architecture, which will allow other researchers to extend and expand these capabilities as new methods become available.

An Evaluation of Alternative Physical Graph Data Designs for Processing Interactive Social Networking Actions

This study quantifies the tradeoff associated with alternative physical representations of a social graph for processing interactive social networking actions. We conduct this evaluation using a graph data store named Neo4j deployed in a client-server (REST) architecture using the BG benchmark. In addition to the average response time of a design, we quantify its SoAR defined as the highest observed throughput given the following service level agreement: 95 % of actions to observe a response time of 100 ms or faster. For an action such as computing the shortest distance between two members, we observe a tradeoff between speed and accuracy of the computed result. With this action, a relational data design provides a significantly faster response time than a graph design. The graph designs provide a higher SoAR than a relational one when the social graph includes large member profile images stored in the data store.

Decoding the Neural Representation of Story Meanings across Languages

Drawing from a common lexicon of semantic units, humans fashion narratives whose meaning transcends that of their individual utterances. However, while brain regions that represent lower‐level semantic units, such as words and sentences, have been identified, questions remain about the neural representation of narrative comprehension, which involves inferring cumulative meaning. To address these questions, we exposed English, Mandarin, and Farsi native speakers to native language translations of the same stories during fMRI scanning. Using a new technique in natural language processing, we calculated the distributed representations of these stories (capturing the meaning of the stories in high‐dimensional semantic space), and demonstrate that using these representations we can identify the specific story a participant was reading from the neural data. Notably, this was possible even when the distributed representations were calculated using stories in a different language than the participant was reading. Our results reveal that identification relied on a collection of brain regions most prominently located in the default mode network. These results demonstrate that neuro‐semantic encoding of narratives happens at levels higher than individual semantic units and that this encoding is systematic across both individuals and languages. Hum Brain Mapp 38:6096–6106, 2017.

Dictionaries and distributions: Combining expert knowledge and large scale textual data content analysis

Theory-driven text analysis has made extensive use of psychological concept dictionaries, leading to a wide range of important results. These dictionaries have generally been applied through word count methods which have proven to be both simple and effective. In this paper, we introduce Distributed Dictionary Representations (DDR), a method that applies psychological dictionaries using semantic similarity rather than word counts. This allows for the measurement of the similarity between dictionaries and spans of text ranging from complete documents to individual words. We show how DDR enables dictionary authors to place greater emphasis on construct validity without sacrificing linguistic coverage. We further demonstrate the benefits of DDR on two real-world tasks and finally conduct an extensive study of the interaction between dictionary size and task performance. These studies allow us to examine how DDR and word count methods complement one another as tools for applying concept dictionaries and where each is best applied. Finally, we provide references to tools and resources to make this method both available and accessible to a broad psychological audience.

Conversation level syntax similarity metric

The syntax and semantics of human language can illuminate many individual psychological differences and important dimensions of social interaction. Accordingly, psychological and psycholinguistic research has begun incorporating sophisticated representations of semantic content to better understand the connection between word choice and psychological processes. In this work we introduce ConversAtion level Syntax SImilarity Metric (CASSIM), a novel method for calculating conversation-level syntax similarity. CASSIM estimates the syntax similarity between conversations by automatically generating syntactical representations of the sentences in conversation, estimating the structural differences between them, and calculating an optimized estimate of the conversation-level syntax similarity. After introducing and explaining this method, we report results from two method validation experiments (Study 1) and conduct a series of analyses with CASSIM to investigate syntax accommodation in social media discourse (Study 2). We run the same experiments using two well-known existing syntactic metrics, LSM and Coh-Metrix, and compare their results to CASSIM. Overall, our results indicate that CASSIM is able to reliably measure syntax similarity and to provide robust evidence of syntax accommodation within social media discourse.

Activities performed by programmers while using framework examples as a guide

It is now a common approach pursued by programmers to develop new software systems using Object-Oriented Application Frameworks such as Spring, Struts and, Eclipse. This improves the quality and the maintainability of the code. Furthermore, it reduces development cost and time. However, the main problem is that these frameworks usually have a complicated Application Programming Interface (API), and typically suffer from the lack of enough documentation and appropriate user manuals. To solve these problems, programmers often refer to existing sample applications of those frameworks to learn how to implement the desired functionality in their own code. This is called the Monkey See, Monkey Do rule in software engineering literature. The aim of this paper is to investigate and analyze the activities programmers perform to achieve a successful use of this rule. The results of this analysis will help us to build automated tools which are helpful for programmers while perusing the aforementioned Monkey See, Monkey Do rule.