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Search Results: 1 - 10 of 898 matches for " Jalal Mahmud "
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Why Do You Spread This Message? Understanding Users Sentiment in Social Media Campaigns
Jalal Mahmud,Huiji Gao
Computer Science , 2014,
Abstract: Twitter has been increasingly used for spreading messages about campaigns. Such campaigns try to gain followers through their Twitter accounts, influence the followers and spread messages through them. In this paper, we explore the relationship between followers sentiment towards the campaign topic and their rate of retweeting of messages generated by the campaign. Our analysis with followers of multiple social-media campaigns found statistical significant correlations between such sentiment and retweeting rate. Based on our analysis, we have conducted an online intervention study among the followers of different social-media campaigns. Our study shows that targeting followers based on their sentiment towards the campaign can give higher retweet rate than a number of other baseline approaches.
Why Are You More Engaged? Predicting Social Engagement from Word Use
Jalal Mahmud,Jilin Chen,Jeffrey Nichols
Computer Science , 2014,
Abstract: We present a study to analyze how word use can predict social engagement behaviors such as replies and retweets in Twitter. We compute psycholinguistic category scores from word usage, and investigate how people with different scores exhibited different reply and retweet behaviors on Twitter. We also found psycholinguistic categories that show significant correlations with such social engagement behaviors. In addition, we have built predictive models of replies and retweets from such psycholinguistic category based features. Our experiments using a real world dataset collected from Twitter validates that such predictions can be done with reasonable accuracy.
Home Location Identification of Twitter Users
Jalal Mahmud,Jeffrey Nichols,Clemens Drews
Computer Science , 2014,
Abstract: 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.
Who Will Retweet This? Automatically Identifying and Engaging Strangers on Twitter to Spread Information
Kyumin Lee,Jalal Mahmud,Jilin Chen,Michelle Zhou,Jeffrey Nichols
Computer Science , 2014,
Abstract: 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 two models: (i) 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; and (ii) a wait-time model based on a user's 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.
Impact of Imbalance Usage of Social Networking Sites on Families
Anika Anwar,Ishrat Ahmed,Tanzima Hashem,Jalal Mahmud
Computer Science , 2015,
Abstract: With the proliferation of social networking sites (SNSs) such as Facebook and Google+, investigating the impact of SNSs on our lives has become an important research area in recent years. Though SNS usage plays a key role in connecting people with friends and families from distant places, SNSs also bring concern for families. We focus on imbalance SNS usage, i.e., an individual remains busy in using SNSs when her family member is expecting to spend time with her. More specifically, we investigate the cause and pattern of imbalance SNS usage and how the emotion of family members may become affected, if they use SNSs in an imbalanced way in a regular manner. This paper is the first attempt to identify the relationship between an individual's imbalance SNS usage and the emotion of her family member in the context of a developing country.
Optimizing The Selection of Strangers To Answer Questions in Social Media
Jalal Mahmud,Michelle Zhou,Nimrod Megiddo,Jeffrey Nichols,Clemens Drews
Computer Science , 2014,
Abstract: Millions of people express themselves on public social media, such as Twitter. Through their posts, these people may reveal themselves as potentially valuable sources of information. For example, real-time information about an event might be collected through asking questions of people who tweet about being at the event location. In this paper, we explore how to model and select users to target with questions so as to improve answering performance while managing the load on people who must be asked. We first present a feature-based model that leverages users exhibited social behavior, including the content of their tweets and social interactions, to characterize their willingness and readiness to respond to questions on Twitter. We then use the model to predict the likelihood for people to answer questions. To support real-world information collection applications, we present an optimization-based approach that selects a proper set of strangers to answer questions while achieving a set of application-dependent objectives, such as achieving a desired number of answers and minimizing the number of questions to be sent. Our cross-validation experiments using multiple real-world data sets demonstrate the effectiveness of our work.
Recommending Targeted Strangers from Whom to Solicit Information on Social Media
Jalal Mahmud,Michelle X. Zhou,Nimrod Megiddo,Jeffrey Nichols,Clemens Drews
Computer Science , 2014,
Abstract: We present an intelligent, crowd-powered information collection system that automatically identifies and asks target-ed strangers on Twitter for desired information (e.g., cur-rent 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.
On the Zeros of Daubechies Orthogonal and Biorthogonal Wavelets  [PDF]
Jalal Karam
Applied Mathematics (AM) , 2012, DOI: 10.4236/am.2012.37116
Abstract: In the last decade, Daubechies’ wavelets have been successfully used in many signal processing paradigms. The construction of these wavelets via two channel perfect reconstruction filter bank requires the identification of necessary conditions that the coefficients of the filters and the roots of binomial polynomials associated with them should exhibit. In this paper, orthogonal and Biorthogonal Daubechies families of wavelets are considered and their filters are derived. In particular, the Biorthogonal wavelets Bior3.5, Bior3.9 and Bior6.8 are examined and the zeros distribution of their polynomials associated filters are located. We also examine the locations of these zeros of the filters associated with the two orthogonal wavelets db6 and db8.
Peculiarities of CO2 exchange in soybean genotypes contrasting in grain yield  [PDF]
Jalal A. Aliyev
Advances in Biological Chemistry (ABC) , 2012, DOI: 10.4236/abc.2012.23039
Abstract: The peculiarities of leaf carbon dioxide gas exchange in soybean genotypes grown in field over a large area and contrasting in duration of vegetation, photosynthetic traits and productivity were studied. Varietal differences in the daily and ontogenetic changes in photosynthesis and photorespiration were identified. It was established that the period of the high activity of photosynthetic apparatus in high productive soybean genotypes lasts for a longer time. The photosynthetic rate and the rate of CO2 release in light due to photorespiration are higher in high productive genotypes. A value of photorespiration in contrasting soybean genotypes constitutes about 28% - 35% of photosynthetic rate. The ratio of gross photosynthesis to photorespiration in genotypes with different productivity is constant enough during ontogenesis, indicating a direct positive correlation between gross photosynthesis and photorespiration. Therefore, contrary to conception arisen during many years on the waste-fulness of photorespiration, taking into account the versatile investigations on different aspects of photo-respiration, it was proved that photorespiration is one of the evolutionarily developed vital metabolic processes in plants and the attempts to reduce this process with the purpose of increasing the crop productivity are inconsistent.
Isolated Area Load Forecasting using Linear Regression Analysis: Practical Approach  [PDF]
M. A. Mahmud
Energy and Power Engineering (EPE) , 2011, DOI: 10.4236/epe.2011.34067
Abstract: This paper presents an analysis to forecast the loads of an isolated area where the history of load is not available or the history may not represent the realistic demand of electricity. The analysis is done through linear regression and based on the identification of factors on which electrical load growth depends. To determine the identification factors, areas are selected whose histories of load growth rate known and the load growth deciding factors are similar to those of the isolated area. The proposed analysis is applied to an isolated area of Bangladesh, called Swandip where a past history of electrical load demand is not available and also there is no possibility of connecting the area with the main land grid system.
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