"When and Where?": Behavior Dominant Location Forecasting with Micro-blog Streams

The proliferation of smartphones and wearable devices has increased the availability of large amounts of geospatial streams to provide significant automated discovery of knowledge in pervasive environments, but most prominent information related to altering interests have not yet adequately capitalized. In this paper, we provide a novel algorithm to exploit the dynamic fluctuations in user's point-of-interest while forecasting the future place of visit with fine granularity. Our proposed algorithm is based on the dynamic formation of collective personality communities using different languages, opinions, geographical and temporal distributions for finding out optimized equivalent content. We performed extensive empirical experiments involving, real-time streams derived from 0.6 million stream tuples of micro-blog comprising 1945 social person fusion with graph algorithm and feed-forward neural network model as a predictive classification model. Lastly, The framework achieves 62.10% mean average precision on 1,20,000 embeddings on unlabeled users and surprisingly 85.92% increment on the state-of-the-art approach.Comment: Accepted as a full paper in the 2nd International Workshop on Social Computing co-located with ICDM, 2018 Singapor

ASPECT-BASED OPINION MINING OF PRODUCT REVIEWS IN MICROBLOGS USING MOST RELEVANT FREQUENT CLUSTERS OF TERMS

Aspect-based Opinion Mining (ABOM) systems take as input a corpus about a product and aim to mine the aspects (the features or parts) of the product and obtain the opinions of each aspect (how positive or negative the appraisal or emotions towards the aspect is). A few systems like Twitter Aspect Classifier and Twitter Summarization Framework have been proposed to perform ABOM on microblogs. However, the accuracy of these techniques are easily affected by spam posts and buzzwords. In this thesis we address this problem of removing noisy aspects in ABOM by proposing an algorithm called Microblog Aspect Miner (MAM). MAM classifies the microblog posts into subjective and objective posts, represents the frequent nouns in the subjective posts as vectors, and then clusters them to obtain relevant aspects of the product. MAM achieves a 50% improvement in accuracy in obtaining relevant aspects of products compared to previous systems

Template-based Abstractive Microblog Opinion Summarisation

We introduce the task of microblog opinion summarisation (MOS) and share a dataset of 3100 gold-standard opinion summaries to facilitate research in this domain. The dataset contains summaries of tweets spanning a 2-year period and covers more topics than any other public Twitter summarisation dataset. Summaries are abstractive in nature and have been created by journalists skilled in summarising news articles following a template separating factual information (main story) from author opinions. Our method differs from previous work on generating gold-standard summaries from social media, which usually involves selecting representative posts and thus favours extractive summarisation models. To showcase the dataset's utility and challenges, we benchmark a range of abstractive and extractive state-of-the-art summarisation models and achieve good performance, with the former outperforming the latter. We also show that fine-tuning is necessary to improve performance and investigate the benefits of using different sample sizes.Comment: Accepted for publication in Transactions of the Association for Computational Linguistics (TACL), 2022. Pre-MIT Press publication versio

PREDICTION IN SOCIAL MEDIA FOR MONITORING AND RECOMMENDATION

Social media including blogs and microblogs provide a rich window into user online activity. Monitoring social media datasets can be expensive due to the scale and inherent noise in such data streams. Monitoring and prediction can provide significant benefit for many applications including brand monitoring and making recommendations. Consider a focal topic and posts on multiple blog channels on this topic. Being able to target a few potentially influential blog channels which will contain relevant posts is valuable. Once these channels have been identified, a user can proactively join the conversation themselves to encourage positive word-of-mouth and to mitigate negative word-of-mouth. Links between different blog channels, and retweets and mentions between different microblog users, are a proxy of information flow and influence. When trying to monitor where information will flow and who will be influenced by a focal user, it is valuable to predict future links, retweets and mentions. Predictions of users who will post on a focal topic or who will be influenced by a focal user can yield valuable recommendations. In this thesis we address the problem of prediction in social media to select social media channels for monitoring and recommendation. Our analysis focuses on individual authors and linkers. We address a series of prediction problems including future author prediction problem and future link prediction problem in the blogosphere, as well as prediction in microblogs such as twitter. For the future author prediction in the blogosphere, where there are network properties and content properties, we develop prediction methods inspired by information retrieval approaches that use historical posts in the blog channel for prediction. We also train a ranking support vector machine (SVM) to solve the problem, considering both network properties and content properties. We identify a number of features which have impact on prediction accuracy. For the future link prediction in the blogosphere, we compare multiple link prediction methods, and show that our proposed solution which combines the network properties of the blog with content properties does better than methods which examine network properties or content properties in isolation. Most of the previous work has only looked at either one or the other. For the prediction in microblogs, where there are follower network, retweet network, and mention network, we propose a prediction model to utilize the hybrid network for prediction. In this model, we define a potential function that reflects the likelihood of a candidate user having a specific type of link to a focal user in the future and identify an optimization problem by the principle of maximum likelihood to determine the parameters in the model. We propose different approximate approaches based on the prediction model. Our approaches are demonstrated to outperform the baseline methods which only consider one network or utilize hybrid networks in a naive way. The prediction model can be applied to other similar problems where hybrid networks exist

Mining Twitter Sequences of Product Opinions with Multi-Word Aspect Terms

Social media platforms have opened doors to users\u27 opinions and perceptions. The text remains the most popular means of contact on social media, despite different means of communication (audio/video and images). Twitter is one such microblogging platform that allows people to express their thoughts within 280 characters per message. The freedom of expression has made it difficult to understand the polarity (Positive, Negative, or Neutral) of the tweets/posts. Given a corpus of microblog texts (e.g., the new iPhone battery life is good, but camera quality is bad ), mining aspects (e.g., battery life, camera quality) and opinions (e.g., good, bad) of these products are challenging due to the vast data being generated. Aspect-Based Opinion Mining (ABOM) is thus a combination of aspect extraction and opinion mining that allows an enterprise to analyze the data in detail, saving time and money automatically. Existing systems such as Hate Crime Twitter Sentiment (HCTS) and Microblog Aspect Miner (MAM) have been recently proposed to perform ABOM on Twitter. These systems generally go through the four-step approach of obtaining microblog posts, identifying frequent nouns (candidate aspects), pruning the candidate aspects, and getting opinion polarity. However, they differ in how well they prune their candidate features. HCTS uses Apriori based Association rule mining to find the important aspects (single and multi word) of a given product. However, the Apriori based system generate many candidate sequences which generates redundant candidate aspects and HCTS also fails to summarize the category of the aspects (Camera? Battery?). MAM follows the similar approach to that of HCTS for finding the relevant aspects but it further clusters the frequent nouns (aspects) to obtain the relevant aspects. However, it does not identify the multi-word aspects and the aspect category of a product. This thesis proposes a system called Microblog Aspect Sequence Miner (MASM) as an extension of Microblog Aspect Miner (MAM) by replacing the Apriori algorithm with the modified frequent sequential pattern mining algorithm. The system uses the power of sequential pattern mining for aspect extraction in ABOM. The sentiments of the tweets are unknown, so we build our approach in an unsupervised learning manner. The input posts are first classified to identify those tweets which contain the opinion (subjective) to those that do not have any opinion (objective). Then we extract the Parts of Speech tags for the explicit aspects to identify the frequent nouns. The novel frequent pattern mining framework (CM-SPAM) is applied to segment the single and multi-word aspects which generates less sequences as compared to previous approaches. This prior knowledge helps us to operate a topic modeling framework (Latent Dirichlet Allocation) to determine the summary of most common aspects (Aspect Category) and their sentiments for a product. Thefindings demonstrate that the MASM model has a promising performance in finding relevant aspects with reduction of average vector size (cost of candidate/aspect generation) against the MAM and HCTS using the Sanders Twitter corpus dataset. Experimental results with evaluation metrics of execution time, precision, recall, and F-measure indicate that our approach has higher recall and precision than the existing systems

Identification of Online Users' Social Status via Mining User-Generated Data

With the burst of available online user-generated data, identifying online users’ social status via mining user-generated data can play a significant role in many commercial applications, research and policy-making in many domains. Social status refers to the position of a person in relation to others within a society, which is an abstract concept. The actual definition of social status is specific in terms of specific measure indicator. For example, opinion leadership measures individual social status in terms of influence and expertise in an online society, while socioeconomic status characterizes personal real-life social status based on social and economic factors. Compared with traditional survey method which is time-consuming, expensive and sometimes difficult, some efforts have been made to identify specific social status of users based on specific user-generated data using classic machine learning methods. However, in fact, regarding specific social status identification based on specific user-generated data, the specific case has several specific challenges. However, classic machine learning methods in existing works fail to address these challenges, which lead to low identification accuracy. Given the importance of improving identification accuracy, this thesis studies three specific cases on identification of online and offline social status. For each work, this thesis proposes novel effective identification method to address the specific challenges for improving accuracy. The first work aims at identifying users’ online social status in terms of topic-sensitive influence and knowledge authority in social community question answering sites, namely identifying topical opinion leaders who are both influential and expert. Social community question answering (SCQA) site, an innovative community question answering platform, not only offers traditional question answering (QA) services but also integrates an online social network where users can follow each other. Identifying topical opinion leaders in SCQA has become an important research area due to the significant role of topical opinion leaders. However, most previous related work either focus on using knowledge expertise to find experts for improving the quality of answers, or aim at measuring user influence to identify influential ones. In order to identify the true topical opinion leaders, we propose a topical opinion leader identification framework called QALeaderRank which takes account of both topic-sensitive influence and topical knowledge expertise. In the proposed framework, to measure the topic-sensitive influence of each user, we design a novel influence measure algorithm that exploits both the social and QA features of SCQA, taking into account social network structure, topical similarity and knowledge authority. In addition, we propose three topic-relevant metrics to infer the topical expertise of each user. The extensive experiments along with an online user study show that the proposed QALeaderRank achieves significant improvement compared with the state-of-the-art methods. Furthermore, we analyze the topic interest change behaviors of users over time and examine the predictability of user topic interest through experiments. The second work focuses on predicting individual socioeconomic status from mobile phone data. Socioeconomic Status (SES) is an important social and economic aspect widely concerned. Assessing individual SES can assist related organizations in making a variety of policy decisions. Traditional approach suffers from the extremely high cost in collecting large-scale SES-related survey data. With the ubiquity of smart phones, mobile phone data has become a novel data source for predicting individual SES with low cost. However, the task of predicting individual SES on mobile phone data also proposes some new challenges, including sparse individual records, scarce explicit relationships and limited labeled samples, unconcerned in prior work restricted to regional or household-oriented SES prediction. To address these issues, we propose a semi-supervised Hypergraph based Factor Graph Model (HyperFGM) for individual SES prediction. HyperFGM is able to efficiently capture the associations between SES and individual mobile phone records to handle the individual record sparsity. For the scarce explicit relationships, HyperFGM models implicit high-order relationships among users on the hypergraph structure. Besides, HyperFGM explores the limited labeled data and unlabeled data in a semi-supervised way. Experimental results show that HyperFGM greatly outperforms the baseline methods on individual SES prediction with using a set of anonymized real mobile phone data. The third work is to predict social media users’ socioeconomic status based on their social media content, which is useful for related organizations and companies in a range of applications, such as economic and social policy-making. Previous work leverage manually defined textual features and platform-based user level attributes from social media content and feed them into a machine learning based classifier for SES prediction. However, they ignore some important information of social media content, containing the order and the hierarchical structure of social media text as well as the relationships among user level attributes. To this end, we propose a novel coupled social media content representation model for individual SES prediction, which not only utilizes a hierarchical neural network to incorporate the order and the hierarchical structure of social media text but also employs a coupled attribute representation method to take into account intra-coupled and inter-coupled interaction relationships among user level attributes. The experimental results show that the proposed model significantly outperforms other stat-of-the-art models on a real dataset, which validate the efficiency and robustness of the proposed model