Huang, Hanyun. / Thesis (Ph.D.)--Chinese University of Hong Kong, 2011. / Includes bibliographical references (leaves 224-242). / Electronic reproduction. Hong Kong : Chinese University of Hong Kong, [2012] System requirements: Adobe Acrobat Reader. Available via World Wide Web. / Abstract also in Chinese; appendix in Chinese.

Li, Ronghua = 社会网络挖掘的算法问题研究 / 李荣华. / Thesis (Ph.D.)--Chinese University of Hong Kong, 2013. / Includes bibliographical references (leaves 157-171). / Electronic reproduction. Hong Kong : Chinese University of Hong Kong, [2012] System requirements: Adobe Acrobat Reader. Available via World Wide Web. / Abstract also in Chinese. / Li, Ronghua = She hui wang luo wa jue de suan fa wen ti yan jiu / Li Ronghua.

Zhang Jiabing. / Thesis submitted in: October 2005. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2006. / Includes bibliographical references (leaves 48-50). / Abstracts in English and Chinese. / Chapter Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- Background --- p.1 / Chapter 1.2 --- Research questions --- p.1 / Chapter 1.3 --- Thesis organization --- p.2 / Chapter Chapter 2 --- Literature Review --- p.4 / Chapter 2.1 --- Conceptualization of social ties --- p.4 / Chapter 2.2 --- Social ties in context --- p.7 / Chapter 2.3 --- Quality of job information --- p.8 / Chapter 2.4 --- Other dimensions of social ties --- p.12 / Chapter 2.5 --- Work ties and the relevancy of job information --- p.14 / Chapter Chapter 3 --- Data and methodology --- p.19 / Chapter 3.1 --- Analytical framework --- p.19 / Chapter 3.2 --- Data --- p.21 / Chapter 3.3 --- Variables --- p.22 / Chapter 3.4 --- Method --- p.25 / Chapter Chapter 4 --- Results --- p.27 / Chapter 4.1 --- Characteristics of work ties --- p.27 / Chapter 4.2 --- Why use work ties? --- p.30 / Chapter 4.3 --- The outcomes of using work ties --- p.39 / Chapter Chapter 5 --- Conclusion and Discussion --- p.44 / Bibliography --- p.48

隨著Web 2.0系統在過去十年的迅猛發展，社會化媒體，比如社會化評分系統、社會化標籤系統、在線論壇和社會化問答系統，已經革命性地改變了人們在互聯網上創造和分享內容的方式。但是，面對社會化媒體數據的飛速增長，用戶面臨嚴重的信息過載的問題。現在，基於社會化媒體學習的社會化計算，已經發展成爲了幫助社會化媒體用戶有效解決信息需求的一個重要的研究領域。一般來說，用戶在社會化媒體中發佈信息，期望通過社會化計算尋找到合適的項目。爲了更好地理解用戶的興趣，分析不同類型的用戶產生數據是非常重要的。另一方面，返回給用戶的可以是項目，或是擁有相似興趣的其他用戶。除了基於用戶的分析，進行基於項目的分析也是非常有趣和重要的，比如理解項目的屬性，將語義相關的項目聚在一起爲了更好地滿足用戶的信息需求等。 / 本論文的目地是提出自動化和可擴展的模型來幫助社會化媒體用戶更有效的解決信息需求。這些模型基於社會化媒體中兩個重要的組成提出：用戶和項目。因此，基於以下兩個目標，我們提出一個統一的框架來整合用戶信息和項目信息：1) 通過用戶的行為找出用戶的興趣，并為之推薦可能感興趣的項目和相似興趣的用戶；2) 理解項目的屬性，並將語義相關的項目聚合在一起從而能更好的滿足用戶的信息需求。 / 爲了完成第一個目標，我們提出了一個新的矩陣分解的框架來整合不同的用戶行為數據，從而預測用戶對新項目的興趣。這個框架有效地解決了數據稀疏性以及傳統方法中信息來源單一的問題，其次，爲了給社會化媒體用戶提供自動發現類似興趣的其他用戶的方式，通過利用社會化標籤信息，我們提出了基於用戶興趣挖掘和基於興趣的用戶推薦的框架。大量的真實數據實驗驗證了提出的基於用戶的模型的有效性。 / 爲了完成第二個目標，我們在具問答性質的社會化媒體中提出了問題推薦的應用。問題推薦的目標是基於一個用戶問題推薦語義相關的問題。傳統的詞袋模型不能有效地解決相關問題中用詞不同的問題。因此，我們提出了兩個模型來結合詞法分析以及潛在語義分析，從而有效地衡量問題間的語義相關度。在問題分析中，當前研究缺少對問題屬性的認識。爲了解決這個問題，我們提出了一個有監督學習的方法來識別問題的主觀性。具體來說，我們提出了一種基於社會化信號的無人工參與的自動收集訓練數據的方法。大量實驗證實了提出的方法的效果超過了之前的其他算法。 / 概括起來，圍繞社會化媒體中兩個重要的組成，我們提出了兩個基於用戶的模型和兩個基於項目的模型來幫助社會化媒體的用戶更準確更有效地解決信息需求。我們通過不同社會化媒體中的大量實驗證實了提出模型的有效性。 / With the astronomical growth of Web 2.0 over the past decade, social media systems, such as rating systems, social tagging systems, online forums, and community-based question answering (Q&A) systems, have revolutionized people’s way of creating and sharing contents on the Web. However, due to the explosive growth of data in social media systems, users are drowning in information and encountering information overload problem. Currently, social computing techniques, achieved through learning with social media, have emerged as an important research area to help social media users find their information needs. In general, users post contents which reflect their interests in social media systems, and expect to obtain the suitable items through social computing techniques. To better understand users’ interests, it is very essential to analyze different types of user generate content. On the other hand, the returned information may be items, or users with similar interests. Beyond the user-based analysis, it would be quite interesting and important to conduct item-oriented study, such as understand items’ characteristics, and grouping items that are semantically related for better addressing users’ information needs. / The objective of this thesis is to establish automatic and scalable models to help social media users find their information needs more effectively. These models are proposed based on the two key entities in social media systems: user and item. Thus, one important aspect of this thesis is therefore to develop a framework to combine the user information and the item information with the following two purposes: 1) modeling users’ interests with respect to their behavior, and recommending items or users they may be interested in; and 2) understanding items’ characteristics, and grouping items that are semantically related for better addressing users’ information needs. / For the first purpose, a novel unified matrix factorization framework which fuses different types of users’ behavior data, is proposed for predicting users’ interests on new items. The framework tackles the data sparsity problem and non-flexibility problem confronted by traditional algorithms. Furthermore, to provide users with an automatic and effective way to discover other users with common interests, we propose a framework for user interest modeling and interest-based user recommendation by utilizing users’ tagging information. Extensive evaluations on real world data demonstrate the effectiveness of the proposed user-based models. / For the second purpose, a new functionality question suggestion, which targets at suggesting questions that are semantically related to a queried question, is proposed in social media systems with Q&A functionalities. Existing bag-of-words approaches suffer from the shortcoming that they could not bridge the lexical chasm between semantically related questions. Therefore, we present two models which combines both the lexical and latent semantic knowledge to measure the semantic relatedness among questions. In question analysis, there is a lack of understanding of questions’ characteristics. To tackle this problem, a supervised approach is developed to identify questions’ subjectivity. Moreover, we come up with an approach to collect training data automatically by utilizing social signals without involving any manual labeling. The experimental results show that our methods perform better than the state-of-theart approaches. / In summary, based on the two key entities in social media systems,we present two user-based models and two item-oriented models to help social media users find their information needs more accurately and effectively through learning with social media. Extensive experiments on various social media systems confirm the effectiveness of proposed models. / Detailed summary in vernacular field only. / Detailed summary in vernacular field only. / Detailed summary in vernacular field only. / Detailed summary in vernacular field only. / Detailed summary in vernacular field only. / Zhou, Chao. / Thesis (Ph.D.)--Chinese University of Hong Kong, 2013. / Includes bibliographical references (leaves 130-163). / Electronic reproduction. Hong Kong : Chinese University of Hong Kong, [2012] System requirements: Adobe Acrobat Reader. Available via World Wide Web. / Abstracts also in Chinese. / Abstract --- p.i / Acknowledgement --- p.vi / Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- Overview --- p.1 / Chapter 1.2 --- Thesis Contribution --- p.9 / Chapter 1.3 --- Thesis Organization --- p.11 / Chapter 2 --- Background Review --- p.15 / Chapter 2.1 --- Recommender System Techniques --- p.15 / Chapter 2.1.1 --- Content-based Filtering --- p.16 / Chapter 2.1.2 --- Collaborative Filtering --- p.17 / Chapter 2.2 --- Information Retrieval Models --- p.23 / Chapter 2.2.1 --- Vector Space Model --- p.24 / Chapter 2.2.2 --- Probabilistic Model and Language Model --- p.27 / Chapter 2.2.3 --- Translation Model --- p.31 / Chapter 2.3 --- Machine Learning --- p.32 / Chapter 2.3.1 --- Supervised Learning --- p.32 / Chapter 2.3.2 --- Semi-Supervised Learning --- p.34 / Chapter 2.3.3 --- Unsupervised Learning --- p.36 / Chapter 2.4 --- Rating Prediction --- p.37 / Chapter 2.5 --- User Recommendation --- p.39 / Chapter 2.6 --- Automatic Question Answering --- p.40 / Chapter 2.6.1 --- Automatic Question Answering (Q&A) from theWeb --- p.40 / Chapter 2.6.2 --- Proliferation of community-based Q&A services and online forums --- p.41 / Chapter 2.6.3 --- Automatic Question Answering (Q&A) in social media --- p.42 / Chapter 3 --- Item Recommendation with Tagging Ensemble --- p.44 / Chapter 3.1 --- Problem and Motivation --- p.44 / Chapter 3.2 --- TagRec Framework --- p.45 / Chapter 3.2.1 --- Preliminaries --- p.45 / Chapter 3.2.2 --- User-Item Rating Matrix Factorization --- p.45 / Chapter 3.2.3 --- User-Tag Tagging Matrix Factorization --- p.47 / Chapter 3.2.4 --- Item-Tag Tagging Matrix Factorization --- p.49 / Chapter 3.2.5 --- A Unified Matrix Factorization for TagRec --- p.50 / Chapter 3.2.6 --- Complexity Analysis --- p.53 / Chapter 3.3 --- Experimental Analysis --- p.54 / Chapter 3.3.1 --- Description of Data Set and Metrics --- p.54 / Chapter 3.3.2 --- Performance Comparison --- p.55 / Chapter 3.3.3 --- Impact of Parameters and --- p.56 / Chapter 3.4 --- Summary --- p.58 / Chapter 4 --- User Recommendation via Interest Modeling --- p.60 / Chapter 4.1 --- Problem and Motivation --- p.60 / Chapter 4.2 --- UserRec Framework --- p.61 / Chapter 4.2.1 --- User Interest Modeling --- p.61 / Chapter 4.2.2 --- Interest-based User Recommendation --- p.65 / Chapter 4.3 --- Experimental Analysis --- p.67 / Chapter 4.3.1 --- Dataset Description and Analysis --- p.67 / Chapter 4.3.2 --- Experimental Results --- p.70 / Chapter 4.4 --- Summary --- p.75 / Chapter 5 --- Item Suggestion with Semantic Analysis --- p.76 / Chapter 5.1 --- Problem and Motivation --- p.76 / Chapter 5.2 --- Question Suggestion Framework --- p.77 / Chapter 5.2.1 --- Question Suggestion in Online Forums --- p.77 / Chapter 5.2.2 --- Question Suggestion in Community-based Q&A Services --- p.84 / Chapter 5.3 --- Experiments And Results --- p.88 / Chapter 5.3.1 --- Experiments in Online Forums --- p.88 / Chapter 5.3.2 --- Experiments in Community-based Q&A Services --- p.96 / Chapter 5.4 --- Summary --- p.105 / Chapter 6 --- Item Modeling via Data-Driven Approach --- p.106 / Chapter 6.1 --- Problem and Motivation --- p.106 / Chapter 6.2 --- Question Subjectivity Identification --- p.107 / Chapter 6.2.1 --- Social Signal Investigation --- p.107 / Chapter 6.2.2 --- Feature Investigation --- p.110 / Chapter 6.3 --- Experimental Evaluation --- p.112 / Chapter 6.3.1 --- Experimental Setting --- p.112 / Chapter 6.3.2 --- Effectiveness of Social Signals --- p.114 / Chapter 6.3.3 --- Effectiveness of Heuristic Features --- p.116 / Chapter 6.4 --- Summary --- p.122 / Chapter 7 --- Conclusion --- p.123 / Chapter 7.1 --- Summary --- p.123 / Chapter 7.2 --- Future Work --- p.124 / Chapter A --- List of Publications --- p.126 / Chapter A.1 --- Conference Publications --- p.126 / Chapter A.2 --- Journal Publications --- p.127 / Chapter A.3 --- Under Review --- p.128 / Bibliography --- p.129

Purpose. The purpose of the study was to examine the network of indigenous resources of the Mexican-American community. Further it was the purpose of the study to explore the knowledge of the respondents regarding the issue of concern. The objectives of this study were threefold. The primary objective was to contribute to the body of knowledge on Mexican-Americans and secondly to explore their network of indigenous resources. The final objective of this study was to contribute to theory development and provide recommendations for social work practice and intervention with Mexican-Americans. Method. The study was exploratory-descriptive, and the setting for the research was Tulare County in the Central San Joaquin Valley, California. The host agency for the study was Tulare County Headstart and Child Care Agency. Thirty-six couples, 18 first generation and 18 second generation were randomly selected as the sample of the study. None of the participants were, past or present, a client of Mental Health Services, which was one of the criteria for the sample selection. Respondents participated in interviews that were prearranged. The interviews were facilitated with a research instrument designed to explore the most salient elements of the network of indigenous resources. Analysis of the data collected was performed by qualitative and quantitative methods. Conclusions. Generally the data revealed that a well defined and functioning system of indigenous resources exist among Mexican-Americans. On most aspects of the indigenous resources and utilization, no difference was determined between the first and second generation respondents. It was found that the sample was youthful and involved in the life tasks of child rearing and family development. Their outlook on life is controlled by a well developed system of belief which is guided by belief in God and evil. Their overall family orientation was extended in nature and in some cases friends and compadres were considered as part or extension of the family. Finally, it was found that curanderos and priests/ministers have a significant role for the respondents in regard to provision of assistance/help for life problems. Recommendations. The findings have implications for social work theory development and social work practice. Sensitivity and awareness is necessary in relation to the cultural, social, and environment of Mexican-Americans. This is of critical importance in the provision of intervention and services. Further social work practitioners need to be cognizant that Mexican-American clients within their relationships and beliefs may possess a wealth of indigenous resources. And a concerted effort must be made to engage the indigenous resources as part of the helping system.

Millions of users share their experiences, thoughts, and interests online, through social media sites (e.g., Twitter, Flickr, YouTube). As a result, these sites host a substantial number of user-contributed documents (e.g., textual messages, photographs, videos) for a wide variety of events (e.g., concerts, political demonstrations, earthquakes). In this dissertation, we present techniques for leveraging the wealth of available social media documents to identify and characterize events of different types and scale. By automatically identifying and characterizing events and their associated user-contributed social media documents, we can ultimately offer substantial improvements in browsing and search quality for event content. To understand the types of events that exist in social media, we first characterize a large set of events using their associated social media documents. Specifically, we develop a taxonomy of events in social media, identify important dimensions along which they can be categorized, and determine the key distinguishing features that can be derived from their associated documents. We quantitatively examine the computed features for different categories of events, and establish that significant differences can be detected across categories. Importantly, we observe differences between events and other non-event content that exists in social media. We use these observations to inform our event identification techniques. To identify events in social media, we follow two possible scenarios. In one scenario, we do not have any information about the events that are reflected in the data. In this scenario, we use an online clustering framework to identify these unknown events and their associated social media documents. To distinguish between event and non-event content, we develop event classification techniques that rely on a rich family of aggregate cluster statistics, including temporal, social, topical, and platform-centric characteristics. In addition, to tailor the clustering framework to the social media domain, we develop similarity metric learning techniques for social media documents, exploiting the variety of document context features, both textual and non-textual. In our alternative event identification scenario, the events of interest are known, through user-contributed event aggregation platforms (e.g., Last.fm events, EventBrite, Facebook events). In this scenario, we can identify social media documents for the known events by exploiting known event features, such as the event title, venue, and time. While this event information is generally helpful and easy to collect, it is often noisy and ambiguous. To address this challenge, we develop query formulation strategies for retrieving event content on different social media sites. Specifically, we propose a two-step query formulation approach, with a first step that uses highly specific queries aimed at achieving high-precision results, and a second step that builds on these high-precision results, using term extraction and frequency analysis, with the goal of improving recall. Importantly, we demonstrate how event-related documents from one social media site can be used to enhance the identification of documents for the event on another social media site, thus contributing to the diversity of information that we identify. The number of social media documents that our techniques identify for each event is potentially large. To avoid overwhelming users with unmanageable volumes of event information, we design techniques for selecting a subset of documents from the total number of documents that we identify for each event. Specifically, we aim to select high-quality, relevant documents that reflect useful event information. For this content selection task, we experiment with several centrality-based techniques that consider the similarity of each event-related document to the central theme of its associated event and to other social media documents that correspond to the same event. We then evaluate both the relative and overall user satisfaction with the selected social media documents for each event. The existing tools to find and organize social media event content are extremely limited. This dissertation presents robust ways to organize and filter this noisy but powerful event information. With our event identification, characterization, and content selection techniques, we provide new opportunities for exploring and interacting with a diverse set of social media documents that reflect timely and revealing event content. Overall, the work presented in this dissertation provides an essential methodology for organizing social media documents that reflect event information, towards improved browsing and search for social media event data.

Social Networks are broad set of actors or organizations and relations between them. The recent review of the literature shows that the research has been focused mainly on the effects of social networks on the entrepreneurial process but little attention is being paid to the process of development of social networks during an entrepreneurial process. The present studies highlight the aspects of process through analysing life cycle, teleology, dialectic and evolutionary views of process of development of social networks. This thesis presents ‘Becoming a Networked Entrepreneur’, a substantive theory of process of social network development in Entrepreneurship Literature constructed using Constructivist Grounded Theory approach to study the 13 entrepreneurs of Fast Growing Firms in Lahore, Pakistan. There are three main conceptual domains of this theory: sources of networks and actions of the entrepreneur and Developmental Patterns. The process of becoming a networked entrepreneur involves constant interaction of entrepreneur with the environment where sources of networks enable the entrepreneur to get connected to a network actor. Through studying the process of becoming a networked entrepreneur, the researchers can view the process in an integrated approach which involves the development of networks before starting the venture and interaction of entrepreneur with the environment where these networks are being developed. The process of becoming a networked entrepreneur presents a framework to study the networks and their development along the entrepreneurial venture.

基于地理位置的社交网络近年得到了非常多的关注。为了提升用戶粘性和吸引用戶，社交問路提供商会提供給用戶基于地理信息的广告和优惠券等服务。方了让广告和优惠券的投递更有效， 预测用戶下个可能访问的地点变得尤为重要。但是，预测地点一个不可避免的挑战就是數一百万计的候选地点构成了庞大的預測空间，使得整个预测过程变成复杂且缓慢。在本论文中，我們利用用戶签到的类別信息对潜在的用戶运动模式進行了建模并提出了一个混合隐马尔可夫模型去预测用戶下个可能访问的地点类别。基于预测出的类别，我們继而对用戶可能访问的地点进行了預測。在类別层次进行建模的好处是能有效地減少候选地点的个數并且能准确地描述用戶行动的实际意义。一般来說，用戶的行为会受到令人偏好的影响，基于这个現象，我們还运用分类的方法对用戶根据其令人愛好的不同進行了划分并对每个组群制定各自的隐马尔可夫模型。实验結果表示如果先预测用可能访问的地点类别，能使得地点预测空间极大地减少预测精度也会变高。 / 在预测用可能访问的地点之后，另外一个很重要的问题是选择将优惠券投递给哪些用从而将产品或地点的影响最大化。在实际运用中，这种将影响最大化的算法会遇到速度上的壁垒。在本论文中，我们研究了在基于地理位置的社交网络中的影响最大化问题，并提出了一个分割方法能有效地提升算法的运行速度。实验结果显示我们的算法在于业界标准方法达到几乎一致的影响力的前提下，能更快地运行。 / Location-based social networks have been gaining increasing popularity in recent years. To increase users’ engagement with location-based services, it is important to provide attractive features, one of which is geo-targeted ads and coupons. To make ads and coupons delivery more effective, it is essential to predict the location that is most likely to be visited by a user at the next step. However, an inherent challenge in location prediction is a huge prediction space, with millions of distinct check-in locations as prediction target. In this thesis we exploit the check-in category information to model the underlying user movement pattern. We propose a framework which uses a mixed hidden Markov model to predict the category of user activity at the next step and then predicts the most likely location given the estimated category distribution. The advantages of modeling on the category level include a significantly reduced prediction space and a precise expression of the semantic meaning of user activities. In addition, as user check-in behaviors are heavily influenced by their preferences, we take a clustering approach to group users with similar preferences, and train a separate hidden Markov model for each group. Extensive experimental results show that, with the predicted category distribution, the number of location candidates for prediction is much smaller, while the location prediction accuracy becomes higher. / Choosing the right users to deliver the coupons and maximizing the influence spread is also an important problem in LBSN, which is called influence maximization problem. In practice speed is an important issue to solve the influence maximization problem. In this thesis, we study the influence maximization problem in location-based social networks and propose a scalable partition approach to solve the influence maximization problem efficiently. Experimental results show that our partition approach achieves quite similar influence spread performance with the original influence maximization approach, while running much faster. / Detailed summary in vernacular field only. / Detailed summary in vernacular field only. / Zhu, Zhe. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2012. / Includes bibliographical references (leaves 93-101). / Abstracts also in Chinese. / Abstract --- p.i / Acknowledgement --- p.vi / Chapter 1 --- Introduction --- p.1 / Chapter 2 --- Background Study --- p.11 / Chapter 2.1 --- Location prediction --- p.11 / Chapter 2.2 --- Influence maximization --- p.16 / Chapter 3 --- User Activity and Location Prediction in Location-based Social Networks --- p.20 / Chapter 3.1 --- Data Analysis --- p.20 / Chapter 3.1.1 --- Data Collection --- p.21 / Chapter 3.1.2 --- Dataset Properties --- p.22 / Chapter 3.2 --- User Activity Prediction --- p.26 / Chapter 3.2.1 --- Definitions --- p.27 / Chapter 3.2.2 --- Category Prediction based on HMM --- p.28 / Chapter 3.2.3 --- Mixed HMM with Temporal and Spatial Covariates --- p.34 / Chapter 3.2.4 --- User Preference Modeling --- p.41 / Chapter 3.3 --- Location Prediction --- p.43 / Chapter 3.4 --- Experimental Evaluation --- p.45 / Chapter 3.4.1 --- Data Preparation --- p.46 / Chapter 3.4.2 --- Category Prediction --- p.47 / Chapter 3.4.3 --- Location Prediction --- p.51 / Chapter 3.5 --- Summary --- p.58 / Chapter 4 --- A Partition Approach to Scalable Influence Maximization in Location-based Social Networks --- p.60 / Chapter 4.1 --- Problem definition --- p.60 / Chapter 4.2 --- Influence probability --- p.62 / Chapter 4.2.1 --- Base model --- p.62 / Chapter 4.2.2 --- Distance and similarity model --- p.65 / Chapter 4.2.3 --- Location entropy model --- p.72 / Chapter 4.3 --- Partition approach --- p.74 / Chapter 4.4 --- Evaluation --- p.79 / Chapter 4.4.1 --- Data preparation --- p.79 / Chapter 4.4.2 --- Precision evaluation --- p.80 / Chapter 4.4.3 --- Influence spread evaluation --- p.83 / Chapter 4.4.4 --- Running time --- p.86 / Chapter 4.5 --- Summary --- p.88 / Chapter 5 --- Conclusion --- p.90 / Bibliography --- p.93

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This diploma thesis concerns the possible ways of use of internet marketing trends and tools in Czech Radio communication activities. In the first step, the thesis will analyse the internet marketing trends in general with regard to the dynamic evolution in the field of internet applications. A special attention will be applied to such terms as new media, social networks and virtual communitites and its use in the field of marketing communications. The theoretical part will be followed by detailed analysis and evaluation of the possible ways of use of these communication tools for the purpose of Czech radio contents promotion and distribution. The aim of this study is to evaluate Czech radio current on-line communication activities with regard to actual internet marketing trends, reveal possible weaknesses and suggest ways of improvement.