Jalal Mahmud

Summarizing sporting events using twitter

The status updates posted to social networks, such as Twitter and Facebook, contain a myriad of information about what people are doing and watching. During events, such as sports games, many updates are sent describing and expressing opinions about the event. In this paper, we describe an algorithm that generates a journalistic summary of an event using only status updates from Twitter as a source. Temporal cues, such as spikes in the volume of status updates, are used to identify the important moments within an event, and a sentence ranking method is used to extract relevant sentences from the corpus of status updates describing each important moment within an event. We evaluate our algorithm compared to human-generated summaries and the previous best summarization algorithm, and find that the results of our method are superior to the previous algorithm and approach the readability and grammaticality of the human-generated summaries.

Home Location Identification of Twitter Users

We present a new algorithm for inferring the home location of Twitter users at different granularities, including city, state, time zone, or geographic region, using the content of users’ tweets and their tweeting behavior. Unlike existing approaches, our algorithm uses an ensemble of statistical and heuristic classifiers to predict locations and makes use of a geographic gazetteer dictionary to identify place-name entities. We find that a hierarchical classification approach, where time zone, state, or geographic region is predicted first and city is predicted next, can improve prediction accuracy. We have also analyzed movement variations of Twitter users, built a classifier to predict whether a user was travelling in a certain period of time, and use that to further improve the location detection accuracy. Experimental evidence suggests that our algorithm works well in practice and outperforms the best existing algorithms for predicting the home location of Twitter users.

Understanding individuals' personal values from social media word use

The theory of values posits that each person has a set of values, or desirable and trans-situational goals, that motivate their actions. The Basic Human Values, a motivational construct that captures peoples values, have been shown to influence a wide range of human behaviors. In this work, we analyze peoples values and their word use on Reddit, an online social news sharing community. Through conducting surveys and analyzing text contributions of 799 Reddit users, we identify and interpret categories of words that are indicative of users value orientations. Using the same data, we further report a preliminary exploration on word-based prediction of Basic Human Values.

Who will retweet this?: Automatically Identifying and Engaging Strangers on Twitter to Spread Information

There has been much effort on studying how social media sites, such as Twitter, help propagate information in different situations, including spreading alerts and SOS messages in an emergency. However, existing work has not addressed how to actively identify and engage the right strangers at the right time on social media to help effectively propagate intended information within a desired time frame. To ad-dress this problem, we have developed two models: (i) a feature-based model that leverages peoplesfi exhibited social behavior, including the content of their tweets and social interactions, to characterize their willingness and readiness to propagate information on Twitter via the act of retweeting; and (ii) a wait-time model based on a users previous retweeting wait times to predict her next retweeting time when asked. Based on these two models, we build a recommender system that predicts the likelihood of a stranger to retweet information when asked, within a specific time window, and recommends the top-N qualified strangers to engage with. Our experiments, including live studies in the real world, demonstrate the effectiveness of our work.

Recommending targeted strangers from whom to solicit information on social media

We present an intelligent, crowd-powered information collection system that automatically identifies and asks targeted strangers on Twitter for desired information (e.g., current wait time at a nightclub). Our work includes three parts. First, we identify a set of features that characterize ones willingness and readiness to respond based on their exhibited social behavior, including the content of their tweets and social interaction patterns. Second, we use the identified features to build a statistical model that predicts ones likelihood to respond to information solicitations. Third, we develop a recommendation algorithm that selects a set of targeted strangers using the probabilities computed by our statistical model with the goal to maximize the over-all response rate. Our experiments, including several in the real world, demonstrate the effectiveness of our work.

Who Will Retweet This? Detecting Strangers from Twitter to Retweet Information

There has been much effort on studying how social media sites, such as Twitter, help propagate information in different situations, including spreading alerts and SOS messages in an emergency. However, existing work has not addressed how to actively identify and engage the right strangers at the right time on social media to help effectively propagate intended information within a desired time frame. To address this problem, we have developed three models: (1) a feature-based model that leverages peoples exhibited social behavior, including the content of their tweets and social interactions, to characterize their willingness and readiness to propagate information on Twitter via the act of retweeting; (2) a wait-time model based on a users previous retweeting wait times to predict his or her next retweeting time when asked; and (3) a subset selection model that automatically selects a subset of people from a set of available people using probabilities predicted by the feature-based model and maximizes retweeting rate. Based on these three models, we build a recommender system that predicts the likelihood of a stranger to retweet information when asked, within a specific time window, and recommends the top-N qualified strangers to engage with. Our experiments, including live studies in the real world, demonstrate the effectiveness of our work.

System U: automatically deriving personality traits from social media for people recommendation

This paper presents a system, System U, which automatically derives peoples personality traits from social media and recommends people for different tasks. The system leverages linguistic signals appearing in a persons social media activities to compute the personality portraits including Big Five personality, fundamental needs and basic human values. This system and technology can be used in a wide variety of personalized applications, such as recommending people to answer questions.

Topika: integrating collaborative sharing with email

New enterprise tools (wikis, team spaces, social tags) offer potential benefits for enterprise collaboration, providing shared resources to organize work. However, a vast amount of collaboration still takes place by email. But email is problematic for collaboration because information may be distributed across multiple messages in an overloaded inbox. Email also increases workload as each individual has to manage their own versions of collaborative materials. We present a novel system, Topika that integrates email with collaboration tools. It allows users to continue to use email while also enjoying the benefits of these dedicated tools. When a user composes an email Topika analyzes the message and suggests relevant shared spaces (e.g., wiki pages) within the users collaboration tools. This allows her to post the email to those spaces. An evaluation of Topikas suggestion algorithm shows that it performs well at accurately suggesting shared spaces.

They Said What?: Exploring the Relationship Between Language Use and Member Satisfaction in Communities

In online communities, satisfied members are essential to community success, since they are more likely to contribute and consume content, engage with other members, and feel committed to the community. However, it is difficult for community leaders to know, on an on-going basis, whether members are satisfied. In this paper, we explore the relationship between member satisfaction and language use within content posted in workplace online communities. We hope to find patterns of language use that are associated with satisfied members. We employ linguistic analysis based on LIWC, and a survey to directly measure member satisfaction in 142 workplace communities. We contribute a better understanding of how members interact in effective workplace communities, and show that linguistic analysis could be a useful part of future methods to automatically assess community member satisfaction.

Design and industrial evaluation of a tool supporting semi-automated website testing

Software testing is the most time‐intensive and resource‐intensive aspect of software development. Can support for testing be improved? This case study describes the motivations and design decisions behind the development of the testing tool, CoTester and its deployment to multiple development teams. CoTester outperforms available testing tools by representing tests using an easy‐to‐understand scripting language and thus making the tests easily editable. The design decisions of the testing tool were derived after conducting a series of interviews with testers and collecting their experiences with manual as well as automated testing. CoTester was developed to support these users, working in an environment of mixed manual and automatic tests, with a progression from manual to automatic testing when circumstances warrant. A series of deployments to four development teams showed that CoTester worked very well for non‐professional testers (i.e. those who do testing only part‐time), and it was also found to be useful by some professional testers. Copyright