ON RELEVANCE FILTERING FOR REAL-TIME TWEET SUMMARIZATION

Real-time tweet summarization systems (RTS) require mechanisms for capturing relevant tweets, identifying novel tweets, and capturing timely tweets. In this thesis, we tackle the RTS problem with a main focus on the relevance filtering. We experimented with different traditional retrieval models. Additionally, we propose two extensions to alleviate the sparsity and topic drift challenges that affect the relevance filtering. For the sparsity, we propose leveraging word embeddings in Vector Space model (VSM) term weighting to empower the system to use semantic similarity alongside the lexical matching. To mitigate the effect of topic drift, we exploit explicit relevance feedback to enhance profile representation to cope with its development in the stream over time. We conducted extensive experiments over three standard English TREC test collections that were built specifically for RTS. Although the extensions do not generally exhibit better performance, they are comparable to the baselines used. Moreover, we extended an event detection Arabic tweets test collection, called EveTAR, to support tasks that require novelty in the system's output. We collected novelty judgments using in-house annotators and used the collection to test our RTS system. We report preliminary results on EveTAR using different models of the RTS system.This work was made possible by NPRP grants # NPRP 7-1313-1-245 and # NPRP 7-1330-2-483 from the Qatar National Research Fund (a member of Qatar Foundation)

English Language Teachers’ Perceptions Regarding Providing Corrective Feedback on Grade 4-8 Students’ Writing in Al Ain Schools, United Arab Emirates.

Writing is one of the skills learners need to acquire using effective strategies. Teaching writing is not an easy task for English teachers because selecting appropriate teaching methods need careful planning, observation, and assessment. Teachers devote time and effort for correcting students’ writing believing that correct feedback might improve students’ writing. This study of 200 Grade 4 to 8 teachers of English in Al Ain explored feedback related to types of errors in students’ writing and type of feedback teachers provide when they respond to students’ writing. Furthermore, this study investigated teachers’ concern associated with providing with providing corrective feedback. English teachers responded to a questionnaire. The results revealed that they tend to respond to all types of errors and most teachers spend a great deal of time responding to students’ writing, focusing on meaning. Additionally, English teachers varied in their responses regarding difficulties they face when providing corrective feedback in writing classes. They reported different kinds of barrier such as time required to provide feedback, students’ understanding of symbols, class management, etc. Other important results indicated that many teachers (M=4.06, SD=.970) were concerned about the time required to respond to students’ writing and a few of them (M=2.27, SD=1.242) reported that providing feedback is boring. Moreover, the data showed that teachers used a ranged of different types of feedback such as writing positive comment, displaying students’ best work, feedback based on students’ needs, and oral feedback. While writing positive comments was the most common, sending electronic feedback to student was the least one. The researcher recommended that providing feedback on students’ writing based on their needs might be better than responding of all types of error. Additionally, it is recommended to focus on meaning and content rather than on form and accuracy. Finally, the researcher suggested that students should be encouraged to use self-assessment and peer assessment after writing. The last recommendation was intended for conducting further studies on this topic

Forward osmosis membranes and processes: A comprehensive review of research trends and future outlook

Recently, Forward Osmosis (FO) desalination process has been widely investigated as a potential technology that could minimize the drawbacks of traditional desalination processes. To review the past, current, and future research scope of the FO desalination process, a statistical analysis that gives insights on the FO topics of interest is needed to assist researchers in the development of the FO technology. The main objective of this work is to conduct a survey highlighting the general and specific research trends in FO technology topics. The level of research interest is quantified based on the number of publications in each area collected from Science Direct and Scopus databases from 1999 to 2020. This survey indicated an increasing number of publications on the FO processes and membranes technology. The topics of interest are fouling phenomenon, draw solutions, membrane fabrication and modification. Some potential research areas highlighted in this review to help researchers to further advance the FO technology. This review reveals that recycling the draw solution and energy consumption are the most important research areas that have shown growth in the number of publications over the last eight years. An increase of publications was also found in the treatment of the organic matter over the last decade. To further promote FO process in industry, developing FO membranes, optimizing the energy consumption, and establishing an effective recovery system are the most essential topics. Thus, the interest in this process is expected to be continued in the future

LOCATION MENTION PREDICTION FROM DISASTER TWEETS

While utilizing Twitter data for crisis management is of interest to different response authorities, a critical challenge that hinders the utilization of such data is the scarcity of automated tools that extract and resolve geolocation information. This dissertation focuses on the Location Mention Prediction (LMP) problem that consists of Location Mention Recognition (LMR) and Location Mention Disambiguation (LMD) tasks. Our work contributes to studying two main factors that influence the robustness of LMP systems: (i) the dataset used to train the model, and (ii) the learning model. As for the training dataset, we study the best training and evaluation strategies to exploit existing datasets and tools at the onset of disaster events. We emphasize that the size of training data matters and recommend considering the data domain, the disaster domain, and geographical proximity when training LMR models. We further construct the public IDRISI datasets, the largest to date English and first Arabic datasets for the LMP tasks. Rigorous analysis and experiments show that the IDRISI datasets are diverse, and domain and geographically generalizable, compared to existing datasets. As for the learning models, the LMP tasks are understudied in the disaster management domain. To address this, we reformulate the LMR and LMD modeling and evaluation to better suit the requirements of the response authorities. Moreover, we introduce competitive and state-of-the-art LMR and LMD models that are compared against a representative set of baselines for both Arabic and English languages

Overview of the CLEF-2019 Checkthat! LAB: Automatic identification and verification of claims. Task 2: Evidence and factuality

We present an overview of Task 2 of the second edition of the CheckThat! Lab at CLEF 2019. Task 2 asked (A) to rank a given set of Web pages with respect to a check-worthy claim based on their usefulness for fact-checking that claim, (B) to classify these same Web pages according to their degree of usefulness for fact-checking the target claim, (C) to identify useful passages from these pages, and (D) to use the useful pages to predict the claim's factuality. Task 2 at CheckThat! provided a full evaluation framework, consisting of data in Arabic (gathered and annotated from scratch) and evaluation based on normalized discounted cumulative gain (nDCG) for ranking, and F1 for classification. Four teams submitted runs. The most successful approach to subtask A used learning-to-rank, while different classifiers were used in the other subtasks. We release to the research community all datasets from the lab as well as the evaluation scripts, which should enable further research in the important task of evidence-based automatic claim verification

Mothers as managers: Work-family balance and identity at the Kuwaiti Ministry of Education.

As a consequence of the discovery of oil in Kuwait in 1940, Kuwaiti culture has undergone major changes. From the 1950s onwards women actively entered the workplace alongside men and this has had a noticeable impact on women's lives. Despite some progress, however, women's struggle for greater equality continues to be influenced by Kuwaiti social and cultural beliefs. This thesis is the first in-depth qualitative analysis of the barriers facing mothers working in management in Kuwait. Specifically, the complex interrelationship between culture, gender and management is explored. It argues that work-family imbalance in Kuwait is a consequence of social and cultural beliefs concerning the status of women in that society. The study classifies the main cultural and gender-related issues affecting the roles of mothers working in management in Kuwait, with a view to helping such women succeed in their working and family lives. The circumstances facing working women in modern, affluent Kuwait while they attempt to raise large families is analysed by reviewing women's status and issues of cultural inequality in modern Kuwaiti society and how this affects their employment. The study adopted a qualitative research methodology to explore factors affecting women's working lives, female identity in the workplace, and work-family balance and conflict. In the first of its two phases, semi-structured interviews were conducted with 27 mothers in management positions at the Kuwaiti Ministry of Education. A life history approach was then taken with another four women to fully investigate how cultural beliefs impact women's rights over their lives and bodies in Kuwaiti society. The main findings indicate that the participants perceived themselves to be affected by work-life imbalance, based on patriarchal ideals being imposed on gender roles, thus creating conflict and destabilising women's self-perceptions in ways which are quite specific to Kuwait. Most importantly, it was found that women in managerial roles tended to reject certain inherent female qualities in themselves and other women, while simultaneously preserving an outward display of traditional femininity. In other words, there was evidence of an unresolved identity crisis. This thesis concludes that women at higher levels of management find it difficult to juggle work and family life because of cultural identity issues in the Kuwaiti context. This problem is significant, as Kuwaiti women would appear to experience more difficulty in this regard than women in many other parts of the world, where the key issues of work-family imbalance are found to be time management, family-friendly policies and the age and number of children. The Kuwaiti Ministry of Education is particularly problematic for women in management roles and it is important to address the issues of work-family balance in Kuwait structurally and institutionally in relation to the family, in order to support women at work

CheckThat! at CLEF 2020: Enabling the Automatic Identification and Verification of Claims in Social Media

We describe the third edition of the CheckThat! Lab, which is part of the 2020 Cross-Language Evaluation Forum (CLEF). CheckThat! proposes four complementary tasks and a related task from previous lab editions, offered in English, Arabic, and Spanish. Task 1 asks to predict which tweets in a Twitter stream are worth fact-checking. Task 2 asks to determine whether a claim posted in a tweet can be verified using a set of previously fact-checked claims. Task 3 asks to retrieve text snippets from a given set of Web pages that would be useful for verifying a target tweet's claim. Task 4 asks to predict the veracity of a target tweet's claim using a set of Web pages and potentially useful snippets in them. Finally, the lab offers a fifth task that asks to predict the check-worthiness of the claims made in English political debates and speeches. CheckThat! features a full evaluation framework. The evaluation is carried out using mean average precision or precision at rank k for ranking tasks, and F1 for classification tasks.Comment: Computational journalism, Check-worthiness, Fact-checking, Veracity, CLEF-2020 CheckThat! La