Constructing Social Knowledge Graph from Twitter Data

Constructing Social Knowledge Graph from Twitter Data   Yue Han Loke 1.1 Introduction The current era of technology allows its users to post and share their thoughts, images, and content via networks through different forms of applications and websites such as Twitter, Facebook and Instagram. With the emerging of social media in our daily lives and it is becoming a norm for the current generation to share data, researchers are starting to perform studies on the data that could be collected from social media [1] [2].The context of this research will be solely dedicated to Twitter data due to its publicly available wealth of data and its public Stream API. Twitters tweets can be used to discover new knowledge, such as recommendations, and relationships for data analysis. Tweets in general are short microblogs consisting of maximum 140 characters that can consists of normal sentences to hashtags and tags with @, other short abbreviation of words (gtg, 2night), and different form of a word (yup, nope). Observing how tweets are posted shows the noisy and short lexical natu re of these texts. This presents a challenge to the flexibility of Twitter data analysis. On the other hand, the availability of existing research conducted on entity extraction and entity linking has decreased the gap between entities extracted and the relationships that could be discovered. Since 2014, the introduction of the Named Entity rEcognition and Linking (NEEL) Challenge [3] has proved the significance of automated entity extraction, entity linking and classification appearing in different event streams of English tweets in the research and commercial communities to design and develop systems that could solve the challenging nature in tweets and to mine semantics from them. 1.2 Project Aim The focus of this research aims to construct a social knowledge graph (Knowledge Base) from Twitter data. A knowledge graph is a technique to analyse social media networks using the method of mapping and measurement for both relationships and information flows among group, organizations, and other connected entities in social networks [4]. A few tasks are required to successfully create a knowledge graph based on Twitter data A method to aid in the construction of knowledge graph is by extracting named entitiessuch as persons, organizations, locations, or brands from the tweets [5]. In the domain of this research, the named entity to be referenced in the tweet is defined as a proper noun or acronym if it is found in the NEEL Taxonomy in the Appendix A of [3], and is linked to an English DBpedia [6] referent and a NIL referent. The second component in creating a social knowledge graph is to utilize those extracted entities and link them to their respective entities in a knowledge base. For example, Tweet: The ITEE department is organizing a pizza gettogether at UQ. #awesome ITEE refers to an organization and UQ refers to an organization as well. The annotation for this is [ITEE, organization, NIL1], where NIL1 refers to the unique NIL referent describing the real-world entity ITEE that does not have the equivalent entry in DBpedia and [UQ, Organization, dbp:University_of_Queensland] which represents the RDF triple (subject, predicate, object). 1.3 Project Goals Firstly, getting the Twitter tweets. This can be achieved by crawling Twitter data using Public Stream API[1] available in the Twitter developer website. The Public Stream API allows extraction of Twitter data in real time. Next, entity extraction and typing with the aid of a specifically chosen information extraction pipeline called TwitIE[2] open-source and specific to social media and has been tested most extensively on microblog sentences. This pipeline receives the tweets as input and recognises the entities in the same tweet. The third task is to link those entities mined from tweets to the entities in the available knowledge base. The knowledge base that has been selected for the context of this project is DBpedia. If there is a referent in DBpedia, the entity extracted will be linked to that referent. Thus, the entity type is retrieved based on the category received from the knowledge base. In the event of the unavailability of a referent, a NIL identifier is given as shown in section 1.2. The selection of an entity linking system with the appropriate entity disambiguation and candidate entity generation that receives the extracted entities from the same Tweet and produce a list with all the candidate entities in the knowledge base. The task is to accurately link the correct entity extracted to one of the candidates. The social knowledge graph is an entity-entity graph combining two extracted sources of entities. The first is the analysis of the co-occurrence of those entities in same tweet or same sentence. Besides that, the existing relationships or categories extracted from DBpedia. Thus, the project aims to combine the extraction of co-occurrence of extracted entities and the extracted relationships to create a social knowledge graph to unlock new knowledge from the fusion of the two data sources. Named Entity Recognition (NER), Information Extraction (IE) are generally well researched in the domain of longer text such as newswire. However, overall, microblogs are possibly the hardest kind of content to process. For Twitter, some methods have been proposed by the research community such as [7] that uses a pipeline approach to perform the first tokenisation and POS tagging and topic models were used to find named entities. [8] propose a gradient-descent graph-based method for doing joint text normalisation and recognition, reaching 83.6% F1 measure. Besides that, entity linking in knowledge graphs have been studied in [9] using graph-based method by collectively gather the referent entities of all named entities in the same document and by modelling and exploiting the global interdependence between Entity Linking decisions. However, the combination of NER, and Entity Linking in Twitter tweets is still a new area of research since the NEEL challenge was first established in 2013 . Based on the evaluation conducted in [10] on the NEEL challenge, lexical similarity mention detection strategy that exploit the popularity of the entities and apply a distance similarity functions to rank entities efficiently, and n-gram [11] features are used. Besides that, Conditional Random Forest (CRF) [12] is another mentioned entity extraction strategy. In the entity detection context, graph distances and various ranking features were used. 2.1. Twitter crawling [13] defined the public Twitter Streaming API provides the ability of collecting a sample of user tweets. Using the statuses/filter API provides a constant stream of public Tweets. Multiple optional parameters may be specified such as language and locations. Applying the method CreateStreamingConnection,a POST request to the API has the capability of returning the public statuses as a stream. The rate limit of the Streaming API allows each application to submit up to 5,000 Twitter. [13] Based on the documentation, Twitter currently allows the public to retrieve at most a 1% sample of their data posted on Twitter at a specific time. Twitter will begin to return the sample data to the user when the number of tweets reaches 1% of all tweets on Twitter. According to [14] research comparing Twitter Streaming API and Twitter Firehouse, the final results of the Streaming API depends strongly on the coverage and the type of analysis that the researcher wishes to perform. For example, the researchers found that if given a set of parameters and the number of tweets matching them increases, the coverage of the Streaming API is reduced. Thus, if the research is concerning a filtered content, the Twitter Firehose would be a better choice with regards to its drawback of restrictive cost. However, since our project requires random sampling of Twitter data without filters except for English language, Twitter Streaming API would be an appropriate choice since it is freely available. 2.2. Entity Extraction [15] suggested an open-source pipeline, called TwitIE which is solely dedicated for social media components in GATE [16]. TwitIE consists for 7 parts: tweet import, language identification, tokenisation, gazetteer, sentence splitter, normalisation, part-of-speech tagging, and named entity recogniser. Twitter data is delivered from the Twitter Streaming API in JSON format. TwitIE included a new Format_Twitter plugin in the most recent GATE codebase which converts the tweets in JSON format automatically into GATE documents. This converter is automatically associated with documents names that end in .json, if not text/x-json-twitter should be specified. The TwitIE system uses TextCat a language processing and identification algorithm for its language identification. It has the capability to provide reliable tweet language identification for tweets written in English using the English POS tagger and named entity recogniser. Tokenisation oversees different characters, class sequence and rules. Since the TwitIE system is dealing with microblogs, it treats abbreviations and URLs as one token each by following the Ritters tokenisation scheme. Hashtags and user mentions are considered as two tokens and is covered by a separate annotation hashtags. Normalisation in TwitIE system is divided into two task: the identification of orthographic errors and correction of the errors found. The TwitIE Normaliser is designed specific to social media. TwitIE reuses the ANNIE gazetteer lists which contain lists such as cities, organisations, days of the week, etc. TwiTie uses the adapted version of the Stanford Part-of speech tagger which is tweets tagged with Penn TreeBank(PTB) tagset trained. The results of using the combination of normalisation, gazetteer name lookup, and POS tagger, the performance was increased to 86.93%. It was further increased to 90.54% token accuracy when the PTB tagset was used. Named entity recognition in TwitIE has a +30% absolute precision and +20% abso lute performance increase as compare to ANNIE, mainly respect to date, Organizations and Person. [7] proposed an innovative approach to distant supervision using topic models that pulls large amount of entities gathered from Freebase, and large amount of unlabelled data. Using those entities gathered, the approach combines information about an entitys context across its mentions. T-NER POS Tagging system called T-POS has added new tags for Twitter specific phenomenal retweets such as usernames, urls and hashtags. The system uses clustering to group together distributionally similar words for lexical variations and OOV words. T-POS utilizes the Brown Clusters and Conditional Random Fields. The combination of both features results in the ability to model strong dependencies between adjacent POS tags and make use of highly correlated features. The results of the T-POS are shown on a 4-fold cross validation over 800 tweets. It is proved that T-POS outperforms the Standford tagger, obtaining a 26% reduction in error. Besides that, when trained on 102K tokens, there is an error reduct ion of 41%. The system includes shallow parsing which can identify non-recursive phrases such as noun, verb and prepositional phrases in text. T-NERs shallow parsing component called T-CHUNK, obtained a better performance at shallow parsing of tweets as compared against the off the shelf OpenNLP chunker. As reported, a 22% reduction in error. Another component of the T-NER is the capitalization classifier, T-CAP, which analyse a tweet to predict capitalization. Named entity recognition in T-NER is divided into two components: Named Entity Segmentation using T-SEG, and classifying named entities by applying LabeledLDA. T-SEG uses IOB encoding on sequence-labelling task to represent segmentations. Furthermore, Conditional Random Fields is used for learning and inference. Contextual, dictionary and orthographic features: a set of type lists is included in the in-house dictionaries gathered from Freebase. Additionally, outputs of T-POS, T-CHUNK and T-CAP, and the Brown clusters are used to generate features. The outcome of the T-SEG as stated in the research paper, Compared with the state-of-the-art news-trained Stanford Named Entity Recognizer. T-SEG obtains a 52% increase in F1 score. To address the issues of lack of context in tweets to identify the types of entities they contain and excessive distinctive named entity types present in tweets, the research paper presented and assessed a distantly supervised approach based on LabeledLD. This approach utilizes modelling of every entity as a combination of types. This allows information about an entitys distribution over types to be shared across mentions, naturally handling ambiguous entity strings whose mentions could refer to different types. Based on the empirical experiments conducted, there is a 25% increase in F1 score over the co-training approach to Named Entity Classification suggested by Collins and Singer (1999) when applie d to Twitter. [17] proposed a Twitter adapted version of Kanopy called Kanopy4Tweets that uses the approach of interlinking text documents with a knowledge base by using the relations between concepts and their neighbouring graph structure. The system consists of four parts: Name Entity Recogniser (NER), Named Entity Linking (NEL), Named Entity Disambiguation(NED) and Nil Resources Clustering(NRC). The NER of Kanopy4Tweets uses a TwitIE a Twitter information extraction pipeline mentioned above. For the Named Entity Linking. For NEL, a DBpedia index is build using a selection of datasets to search for suitable DBpedia resource candidates for each extracted entity. The datasets are store in a single binary file using HDT RDF format. This format has compact structures due to its binary representation of RDF data. It allows for faster search functionality without the need of decompression. The datasets can be quickly browse and scan through for a specific object, subject or predicate at glance. For e ach named entity found by NER component, a list of resource candidates retrieved from DBpedia can be obtain using the top-down strategy. One of the challenges found is the large volume of found resource candidates impacts negatively on the processing time for disambiguation process. However, this problem can be resolved by reducing the number of candidates using a ranking method. The proposed ranking method ranks the candidates according to the document score assigned by the indexing engine and selects the top-x elements. The NED takes an input of a list of named entities which are candidate DBpedia resources after the previous NEL process. The best candidate resource for each named entity is selected as output. A relatedness score is calculated based on the number of paths between the resources weighted by the exclusivity of the edges of these paths which is applied to candidates with respect to the candidate resources of all other entities. The input named entities are jointly dis ambiguated and linked to the candidate resources with the highest combined relatedness. NRC is a stage whereby if there are no resource in the knowledge base that can be linked to a named entity extracted. Using the Monge-Elkan similarity measure, the first NIL element is assign into a new cluster, then the next element is used to differentiate from the previous ones. An element is added to a cluster when the similarity between an element and the present clusters is above a fixed threshold, the element is added to that particular cluster, whereas a new cluster is formed if there are no current cluster with a similarity above the threshold is found. 2.3. Entity Extraction and Entity Linking [18]proposed a lexicon-based joint Entity Extraction and Entity Linking approach, where n-grams from tweets are mapped to DBpedia entities. A pre-processing stage cleans and classifies the part-of-speech tags, and normalises the initial tweets converting alphabetic, numeric, and symbolic Unicode characters to ASCII equivalents. Tokenisation is performed on non-characters except special characters joining compound words. The resulting list of tokens is fed into a shingle filter to construct token n-grams from the token stream. In the candidate mapping component, a gazetteer is used to map each token that is compiled from DBpedia redirect labels, disambiguation labels and entities labels that is linked to their own DBpedia entities. All labels are lowercase indexed and linked by exact matches only to the list of candidate entities in the form of tokens. The researcher used a method of prioritizing longer tokens than shorter ones to remove possible overlaps of tokens. For each entity ca ndidate, it considers both local and context-related features via a pipeline of analysis scorers. Examples of local features included are string distance between the candidate labels and the n-gram, the origin of the label, its DBpedia type, the candidates link graph popularity, the level of uncertainty of the token, and the surface form that matches best. On the other hand, the relation between a candidate entity and other candidates with a given context is accessed by the context-related features. Examples of mentioned context-related features are direct links to other context candidates in the DBpedia link graph, co-occurrence of other tokens surface forms in the corresponding Wikipedia article of the candidate under consideration, co-references in Wikipedia article, and further graph based feature of the link graph induced by all candidates of the context graph which includes graph distance measurements, connected component analysis, or centrality and density observations. Besid es that, the candidates are sorted per their confidence score based on how an entity describes a mention. If the confidence score is lower than the threshold chosen, a NIL referent is annotated. [19] proposed a lexical based and n-grams features to look up resources in DBpedia. The role of the entity type was assigned by a Conditional Random Forest (CRF) classifier, that is specifically trained using DBpedia related feature (local features), word embedding (contextual features), temporal popularity knowledge of an entity extracted from Wikipedia page view data, string similarity measures to measure the similarity between the title of the entity and the mention (string distance), and linguistic features, with additional pruning stage to increase the precision of Entity Linking. The whole process of the system is split into five stages: pre-processing, mention candidate generation, mention detection and disambiguation (candidate selection), NIL detection and entity mention typing prediction. In the pre-processing stage, tweet tokenisation and part-of-speech tags were used based on ARK Twitter Part-of-Speech Tagger, together with the tweet timestamps extracted from tweet ID. Th e researchers used an in-house mention-entity dictionary of acronyms. This dictionary computes the n-grams (n [20] research paper proposed an entity linking technique to link named entity mentions appearing in Web text with their corresponding entities in a knowledge base. The solution mentioned is by employing a knowledge base. Due to the vast knowledge shared among communities and the development of information extraction techniques, the existence of automated large scale knowledge bases has been ensured. Thus, this rich information about the worlds entities, their relationships, and their semantic classes which are all possibly populated into a knowledge base, the method of relation extraction techniques is vital to obtain those web data that promotes discovery of useful relationships between entities extracted from text and their extracted relation. Once possible way is to map those entities extracted and associated them to a knowledge base before it could be populated into a knowledge base. The goal of entity linking is to map ever textual entity mention m à ¢Ã‹â€ Ã‹â€  M to its corres ponding entry e à ¢Ã‹â€ Ã‹â€  E in the knowledge base. In some cases, when the entity mentioned in text does not have its corresponding entity record in the given knowledge base, a NIL referent is given to indicate a special label of un-linkable. It is mentioned in the paper that named entity recognition and entity linking o be jointly perform for both processes to strengthen one another. A method proposed in this paper is candidate entity generation. The objective of the entity linking system is to filter out irrelevant entities in the knowledge base that for each entity extracted. A list of candidates which might be the possible entities that the extracted entity is referring to is retrieved. The paper suggested three techniques to handle this goal such as name based dictionary techniques entity pages, redirect pages, disambiguation pages, bold phrases from the first paragraphs, and hyperlinks in Wikipedia articles. Another method proposed is the surface form expansion from the local document that consists of heuristics based methods and supervised learning methods, and methods based on search engine. In the context of candidate entity ranking method, five categories of methods are advised. The supervised ranking methods, unsupervised ranking methods, independent ranking methods, collective ranking methods and collaborative ranking methods. Lastly, the research paper mentioned ways to evaluate entity linking systems using precision, recall, F1-measure and accuracy. Despite all these methods used in the three main approaches is proposed to handle entity linking system, the paper clarified that it is still unclear which are the best techniques and systems. This is since different entity linking system react or perform differently according to datasets and domains. [21] proposed a new versatile algorithm based on multiple addictive regression trees called S-MART (Structured Multiple Additive Regression Trees) which emphasized on non-linear tree-based models and structured learning. The framework is a generalized Multiple Addictive Regression Trees (MART) but is adapted for structured learning. This proposed algorithm was tested on entity linking primarily focused on tweet entity linking. The evaluation of the algorithm is based on both IE and IR situations. It is shown that non-linear performs better than linear during IE. However, for the IR setting, the results are similar except for LambdaRank, a neural network based model. The adoption of polynomial kernel further improves the performance of entity linking by non-LINEAR SSVM. The paper proved that entity linking of tweets perform better using tree-based non-linear models rather than the alternative linear and non-linear methods in IE and IR driven evaluations. Based on the experiments condu cted, the S-MART framework outperforms the current up-to-date entity linking systems. 2.4. Entity Linking and Knowledge Base Based on [22], an approach to free text relation extraction was proposed. The system was trained to extract the entities from the text from existing large scale knowledge base in a cooperatively manner. Furthermore, it utilizes the learning of low-dimensional embedding of words, entities and relationships from a knowledge base with regards to score functions. Built upon the norm of employing weakly labelled text mention data but with a modified version which extract triples from the existing knowledge bases. Thus, by generalizing from knowledge base, it can learn the plausibility of new triples (h, r, t); h is the left-hand side entity (or head), the right-hand side entity (or tail) and r the relationship linking them, even though this specific triple does not exist. By using all knowledge base triples rather than training only on (mention, relationship), the precision on relation extraction was proved to be significantly improved. [1] presented a novel system for named entity linking over microblog posts by leveraging the linked nature of DBpedia as knowledge base and using graph centrality scoring as disambiguation methods to overcome polysemy and synonymy problems. The motivation for the authors to create this method is because linked entities tend to appear in the same tweets because tweets are topic specific and together with the assumption since tweets are topic specific, related entities tend to appear in the same tweet. Since the system is tackling noisy tweets acronyms handling and Hashtags in the process of entity linking were integrated. The system was compared with TAGME, a state-of-the-art system for named entity linking designed for short text. The results shown that it outperformed TAGME in Precision, Recall and F1 metrics with 68.3%, 70.8% and 69.5%. [23] presented an automated method to populate a Web-scale probabilistic knowledge base called Knowledge Vault (KV) that uses the combination of extractions from the Web such as text documents (TXT), HTML trees (DOM), Html tables (TBL), and Human Annotated pages (ANO). By using RDF triples (subject, predicate, object) with association to a confidence score that represents the probability that KV believes the triple is correct. In addition, all 4 extractors are merged together to form one system called FUSED-EX by constructing a feature vector for each extracted triple. Next, a binary classifier is applied to compute the formula. The advantages of using this fusion extractor is that it can learn the relative reliabilities of each system as well as creating a model of the reliabilities. The benefits of combining multiple extractors include 7% higher confidence triples and a high AUC score (the higher probability that a classifier will choose a randomly chosen positive instance to be ra nked) of 0.927. To overcome the unreliability of facts extracted from the Web, prior knowledge is used. In the domain of this paper, Freebase is used to fit the existing models. Two ways were proposed in the paper which are Path ranking algorithm with AUC scores of 0.884 and the Neural network model with a AUC score of 0.882. A fusion of both methods stated was conducted to increase performance with an increased AUC score of 0.911. With the evidence of the benefits of fusion quantitatively, the authors of the paper proposed another fusion of the prior methods and the extractors to gain additional performance boost. The result of the fusion is a generation of 271M high confidence facts with 33% new facts that are unavailable in Freebase. [24]proposed TremenRank, a graph based model to tackle the target entity disambiguation challenge, task of identifying target entities of the same domain. The motivation of this system is due to the challenges and unreliability of current methods that relies on knowledge resources, the shortness of the context which a target word occurs, and the large scale of the document collected. To overcome these challenges, first TremenRank was built upon the notion of collectively identity target entities in short texts. This reduces memory storage because the graph is constructed locally and is continuously scale-up linearly as per the number of target entities. This graph was created locally via inverted index technology. There are two types of indexes used: the document-to-word index and the word-to-document index. Next, the collection of documents (the shorts texts) are modelled as a multi-layer directed graph that holds various trust scores via propagation. This trust score provided an in dication of the possibility of a true mention in a short text. A series of experiments was conducted on TremenRank and the model is more superior than the current advanced methods with a difference of 24.8% increase in accuracy and 15.2% increase in F1. [25]introduced a probabilistic fusion system called SIGMAKB that integrates strong, high precision knowledge base and weaker, and nosier knowledge bases into a single monolithic knowledge base. The system uses the Consensus Maximization Fusion algorithm to validate, aggregate, and ensemble knowledge extracted from web-scale knowledge bases such as YAGO and NELL and 69 Knowledge Base Population. The algorithm combines multiple supervised classifiers (high-quality and clean KBs), motivated by distant supervision and unsupervised classifiers (noisy KBs) Using this algorithm, a probabilistic interpretation of the results from complementary and conflicting data values can be shown in a singular response to its user. Thus, using a consensus maximization component, the supervised and unsupervised data collected from the method stated above produces a final combined probability for each triple. The standardization of string named entities and alignment of different ontologies is done in the pre-processing stage. Project plan Semester 1 Task Start End Duration(days) Milestone Research: 23/03/2017 Twitter Call 27/02/2017 02/03/2017 4 Entity Recognition 27/02/2017 02/03/2017 4 Entity Extraction 02/03/2017 02/03/2017 7 Entity Linking 09/03/2017 16/03/2017 7 Knowledge Base Fusion 16/03/2017 23/03/2017 7 Proposal 27/02/2017 30/03/2017 30 30/03/2017 Crawling Twitter data using Public Stream API 31/03/2017 15/04/2017 15 15/04/2017 Collect Twitter data for training purp

Missiles in Cuba: Thirteen Days, Robert F. Kennedy :: essays research papers

  Ã‚  Ã‚  Ã‚  Ã‚  This novel tells the story of a small-town, working-class life in the mid 1900’s. The daughter of a meatpacking company millwright, Cheri Register tells about the event, which divides her small town of Albert Lea during time of depression. Albert Lea, Minnesota was an industrial town of only 13,545 people. Surrounding the area was cornfields, lakes, cattail marshes, knolls, and oak groves.   Ã‚  Ã‚  Ã‚  Ã‚  Albert Lea still was in the 1950’s when this story began. Cheri was an elementary student. Her school plans many field trips, which tend to be excursions in industrial technology. Cheri and her classmates visit places, which serve an entertainment and educational purpose.  Ã‚  Ã‚  Ã‚  Ã‚  They have visited placed such as printing press, Coca-Cola, and egg hatching victories. Their next trip was different. Cheri and her class were to visit the Wilson & Co. meatpacking industry. This was where Cheri’s father had worked since 1943. Not speaking much of his job Cheri didn’t know much about her fathers work. This trip consisted of a parent’s signature because of the scene it may bring to the youngsters. Each kid had the option not to participate in the activity that morning since once they entered there was no turning back. They describe the trip as very scenic and educational. For the rest of the day the kids talked to each other about th e incredible views, which they encountered. Such as the hundreds of people who worked there and how the assembly lines for the animals just never ended, one after another after another.   Ã‚  Ã‚  Ã‚  Ã‚  Wilson & Co. was a gigantic industrial factory in which many of the mid-class working life men supported their families by. During this time the eight-hour working day laws were supported and workers did just that. An eight-hour day at Wilson & Co. itself was extremely demanding and tiresome to the workingman. Cheri’s dad at this time was in a verbatim pattern of a workday, dinner, and falling asleep attempting to relaxing. Wilson & Co. decided to demand â€Å"mandatory overtime† of two hours. This made workers furious. Cheri’s dad himself would often work overtime just for the cash for odds and end payments needed around the house. Workers fought their boss in saying they didn’t have to obey this demand. Wilson & Co. reacted with a â€Å"yellow-dog contract† threatening if their workers didn’t sign agreeing to work these extra hours then they would be asked to leave the plant and not return.

Macbeth: Corruption :: Macbeth essays

Macbeth: Corruption When people come into a postion of power where the definition of control becomes a new definition according to their point of view, they unleash a feeling in their minds that what ever decision they make that directly conflicts the lives of other people, they don't feel responsible. That's when npower corrupts the minds of people. People in power feel that they can do anything when their in power for a long period of time. Corruption is something that is motivated by greed and deception. It's a very sinister personality that controls and destroys people's live and makes them the kind of person other people don't want to associate with. When a person is in power, some situations occur when they can acquire anything, lying, bribing, coning, or stealing. With these in mind anybody in power could become a powerful foe. The reason why corruption has become a problem is because it's fair to become greedy for more. And soon it gets out of control and now you have a corrupted person who in order to change would have to step out of power and become a person who doesn't control. Nothing can really be done to sustain it or avoid it, if you take a corrupt person in power and replace a fair and just person. Sooner or later they also become corrupt. You just have to assume and hope the replacement will be a fair person. In the tradgity "Macbeth" there are many examples of corruption. When Macbeth became Thane of Cowdor his wife, Lady Macbeth, was very delighted to hear of such news. And when hearing that Duncan, the king ,would be coming to dinner at their castle, gave her an idea that maybe they need a new king, Macbeth! This is a perfect example of corruption, as soon as she became more powerful, she was lusting for more. As play carries on the corruption becomes greater with the killing of Banquo. Banquo who was a friend of Macbeth is betrayed when Macbeth, the new king, orders him to be killed only because Banquo has a son named Fleance that Macbeth stupidly believes will take the throne away from him,"To make them kings, the seeds of Banquo kings!" This of course get the people suspicious, but now it's to late, Macbeth had gone crazy as well as Lady Macbeth. Of course then when your in power sometimes you think nothing will happen that will hurt you in any way, that your safe in the confinment of your office or room. Of course this never works and theres always some terrible thing bound to

Physical Education and Academic Achievement in 10th grade Essay

Physical education in high schools in the United States of America is the one branch of academic filed which is gaining fast attention on the side of the Government policy, the schools administration. More and more emphasis-shift is being observed toward orientation of the students and their parents with regard to the importance of physical education and its connection with real life, health, and, above all, with higher academic achievement. In schools, although all grades bear importance with relation to physical education and higher academic achievement and attendance rate, it is, however, the ninth and tenth-grades where there is a turning point for the students. Therefore, it is the time for the schools administration to pay more attention toward the planning and application of the physical education in these grades. â€Å"Physical activity is critical to the development and maintenance of good health. The goal of physical education is to develop physically educated individuals who have the knowledge, skills, and confidence to enjoy a lifetime of healthful physical activity† (aahperd. org) It is apt to say that the way their physical education is planned and implemented required different parameters. This is so because the demands of students are different. They need to be exposed to such physical activities which not only excite them but also create a sense of responsibility in them; the main focus of such activities should then be to prepare them for real-life situation and challenges. Read more:  Physical Education Essays The present paper examines the link between physical education in 10th-grade and higher academic achievement in secondary schools of the United States of America. It also focuses on the link between physical education and its advantages when it comes to higher attendance rate and so on. The paper also investigates the findings of some major studies conducted in the area of academic achievement and its hypothesized relationship with such other diverse areas as after-school pursuits, homework phenomena, types of activities in and out of school, cross gender and ethnicity issues. This is to give the reader a proper understanding of the major issues in the present debate of achievement in high school and particularly of the tenth-graders. Review of Literature Physical education is a â€Å"systematic instruction in sports, exercises, and hygiene given as part of a school or college program† (infoplease. com). Thus, there are certain goals to be met by implementing the physical education scenario. There have been considerable attempts in encircling the physical education curriculum in the United States of America. However, it is only recently that more and more emphasis is being laid on the importance of physical education and its link between higher academic achievement and proportionate significance of attendance rate. Since the year 1987, NASPE (the National Association for Sport and Physical Education) has been in constant pursuit in the updated information about the awareness in public of physical education in US education system. There are certain reservations that come to us in this regard. Three of them are mainly discussed in the present literature about physical education in the US educational system. These are: (1) There is no federal law, as yet, which says that physical education is to be provided to the students in the US education system; also there are not listed any incentives to offer physical education programs. 2) Although states may come up with some ground for the physical education policies, the state schools are free to work on their own in promoting, retaining, reducing the physical education portfolio in their own way. ) Another issue is that many states let go the responsibility for all content taught in schools to local school districts. (pe4life. com) The evidence is also found on other literature and net resources that plainly make it public that in the United States of America, physical education is not something prioritized. Even physical education in high school and elsewhere are â€Å"in sorry shape† and that the physical education â€Å"has been squeezed out of school by new curriculum requirements and other factors† (ducationworld. om). Apart from this, there is now word by such professionals as physicians who see that physical education is very necessary for a better future generation. More solid evidence comes form the Report to the President: Promoting Better Health for Young People through Physical Activity and Sports. In this report, Donna Shalala, Health and Human Services Secretary, and Richard Riley, Education Secretary, boldly wrote: â€Å"Our nation’s young people are, in large measure, inactive, unfit, and increasingly overweight. This report should stimulate action to make sure that daily physical activity for young people becomes the norm in our nation† (pe4life. com). Although we can see that today more and more attention is being paid to the physical education policy in US education system, there are grave critical areas that need to be addressed for a better physical education policy. For example there are trends of abating physical education time in schools because of pressure from academic side. This is very much a problem present in today’s education system. The simple fact is that more and more research findings are coming along with results that show that the old maxim â€Å"sound mind in a sound body† is aptly right. Thus it is now acknowledged that the students who are physically fit perform better on the academic side as well. In this connection we see that: â€Å"a 2002 California Department of Education study found a direct correlation between higher levels of physical fitness and higher academic test scores. According to Delaine Eastin, California State Superintendent of Public Instruction, â€Å"We now have the proof we’ve been looking for: students achieve best when they are physically fit† (pe4life. com (b)). As such, a new educational approach is now being said to be coming up on the educational front of the US schools that is called ‘total mind, total body educational approach’. In addition to the above, U. S. Surgeon General Report states that â€Å"Nearly two out of three adult Americans, or 130 million of us, are overweight or obese†; as such The health problems related to our growing girths are well known – more heart diseases, diabetes and other weigh-related ailments that send 300,000 Americans to an early grave every year. In fact, if childhood obesity continues to grow at current levels, some health officials contend, this generation of youngsters may actually have a shorter lifespan than their parents – a first in history† (smc. edu). When the report gives this alerting call to the nation, it is of much relief that in the same report the U. S. surgeons also carve out some strategies to overcome the problem. Among these strategies, the most prominent place is give to the physical education for the youths in the high schools. However, this is also not wrong to state that with the advent of the recognition of physical education in high schools, specially in ninth- and tenth-grades, another debate has taken birth: Whether or not physical education grade be included in high school students’ GPA. This school of thought considers that inclusion of physical education grades in student’s GPA will boost up students’ morale and will encourage them to do better in the physical education class because â€Å"Physical education is truly a microcosm of life, and every lesson taught has the potential to translate into the â€Å"real world† and prepare students to become healthy, productive members of society† (elibrary. bigchalk. com). Whatever the arguments are, we can be certain that in today’s literature about physical activity and high schools, physical education is something that needs more and more space in schools policies so that a better tomorrow can be handed down to our future generation that is already getting more pressure from academic side. If we look at the schools curriculum regarding physical education of ninth- and tenth-grades, we find that there are no serious attempts at defining the education. For example, a school in US states only three basic goals for physical education for the ninth- and tenth-grades (benton. k12. wi. us). Another school very shortly gives only the names of the physical activities to be observed by the students (rockingham. k12. va. us). This kind of treatment is in abundance and needs serious attention on the part of the government and school administrations to bring solid measures for the improvement in this area. Physical Education and Academic Achievement in 10th-grade When it comes to physical education in high schools, we find that it is very important for people who are in grades ninth and tenth because â€Å"This is an age when students begin to impose self-judgement in terms of accepting their physical appearance and their willingness to involve themselves in positive physical activities† (teacherweb. com). According to the Physical Activity and Health (a report of the surgeon genera) there is deep linkage between physical education and academic achievement. Physical education makes it possible for a better and healthy start in life enabling students to go for challenges that are not touched upon by those who are not active through physical activity. Moreover, the report suggests that schools should create such programs that can offer students opportunities by which they can get into physical activity. At this stage, this becomes more important because that report informs that as people grow, they show more and more declination in goring for physical activity: â€Å"Physical activity declines dramatically with age during adolescence† (cdc. gov). Thus, there is stark need that students of ninth and tenth-grade must be made aware of the importance of physical education and their academic achievement. The physical education should also be given priority by the school administration so that a sound educational environment can be achieved for healthier and academically better grounds for the students of high schools. This has been found through the research that physical activities may enhance cognitive functioning of the brain. This, as a result, may explain the relationship found between the students’ involvement in physical activities and their academic success (nps. 12. va. us). There are three major findings in the literature that relate physical activity involvement and the cognitive functioning. These are: 1) There is a significantly â€Å"positive relationship between physical activity and cognitive functioning in children†; 2) â€Å"Results support possibility that participation in physical activity causes improvements in cognitive function†; 3) â€Å"Acute bouts of physical activity exert short-term positive benefits on the behavioural and cognitive functioning of youths†; ) It has also been found that â€Å"Being on a school sports team and having a positive achievement orientation were positively associated with physical activity levels† (holidaycalendar. dsr. wa). Therefore, from this very analysis it becomes pretty clear that in 10th grade (or roughly in high school), physical education plays a very important role as far as the phenomena of long, healthy life is concerned; as far as real-life active participation by the youth is concerned; and, above all, their academic achievement is concerned. However, looking at the picture more closely, we find that the present scenario in the United States of America, when it comes to physical education, academic success, link between physical education and academic success and real-life situation; there are certain challenges present to the Government. For example, â€Å"Nearly half of young people aged 12-21 are not vigorously active on a regular basis and physical activity declines dramatically with age during adolescence† (arlington. k12. va. us). And this has surely given the government a call of high alert because it is simply suggested that less physical activity on the side of the youth will give rise to ailments that will in return proportionately affect academic achievement of the students. Thus, today we can see that the United States of America is building more solid policies for coping up with the situation. In this very connection, a recent example is the President’s Challenge program, which purely focuses on bringing the people of the U.  S. to the physical activity grounds so that a healthier society for the future can be the possibility. This is â€Å"a program that encourages all Americans to make being active part of their everyday lives. No matter what your activity and fitness level, the President’s Challenge can help motivate you to improve† (presidentschallenge. org). The Presidential Challenge program is all about remaining with an active lifestyle and the program specifically focuses on how to assess the citizens in this regard. This program also introduces some rewards for the participants of the program like PALA (Presidential Active Lifestyle Award (presidentschallenge. org). This simply gives us the idea that the government of the U. S. has come up with grave policies which focus on the physical fitness of the citizens for a better tomorrow and a healthy society. Physical Education and Academic Achievement Debate  There are different schools of thoughts within the research that circle around the relationship between academic achievement in students and such other phenomena as physical education, homework, social loafing, and so forth. A considerable area of research exists that especially takes into examination the students of eighth to tenth grade. For example the article After-school pursuits, ethnicity, and achievement for 8th- and 10th-grade students which is one of the remarkable piece showing the contribution of people like James B.  Schreiber , Elisha A. Chambers, Walberg, Paschal, & Weinstein, Cooper, and so on. Thus the proponents of homework suggest that â€Å"Homework has been shown to have a positive relationship with† academic achievement. And that there is also a proportionate difference between the amount of time spent on homework (After-School Pursuits). However, the other side of the pole comes up with the argument that there is not total contribution that can be related to homework alone when looking into the matter of higher academic achievement. This school of though aptly analyzes the effects of physical education on academic achievement of the students. For example, a research review by Holland and Andre in the year 1987 brought into their examination the relationship, if any, between physical education or athletic participation and academic achievement in the students. Their findings brought some more elements of debate in the intellectual circle because they found that there were sex-related differences among students as far as physical education and academic achievement is concerned. They reported that the research demonstrated that male high school athletes received somewhat higher grade point averages (GPAs) than did nonathletes. However, when one considers standardized achievement or aptitude tests, boys whose only after-school activity was sports scored lower than national averages on the Standardized achievement Test. No significant differences in GPAs or standardized tests were observed between female athletes and nonathletes† (After-School Pursuits).

DIstinctive Voices Essay Essay

How does the use of distinctive voices emphasise the ways that individuals respond to significant aspects of life? In your response, make detailed reference to your prescribed text Severn Cullis- Suzuki and J.F. Kennedy and ONE other related text of your own choosing. Distinctive voices provide understanding and emphasise the significant events and aspects of life in relation to the individual and their underlying place in the society. Both John F. Kennedy and Severn Cullis Suzuki provide evidence of this which is evident in the use of contrast, anaphora, imagery, rhetorical questions and allusion but is also perpetuated in The Sharpness of Death by Gwen Harwood. These texts provide understanding and connections within eachother†¦Ã¢â‚¬ ¦.. Distinctive Voices engage with the audience to create an understanding with people about current events. The Address to the Plenary Session, Earth summit speech spoken by Severn Cullis-Suzuki is using a remonstrative voice to point out the issues in the environment today, she points out how important this earth is and how it is shared and illustrates the hypocrisy of adults in values they instill in children but fail to execute themselves. â€Å"You don’t know how to fix the holes in our ozone layer. You don’t know how to bring salmon back up our dead stream. You don’t know how to bring back animals that are now extinct† this use of anaphora clearly highlights both the problems many places on earth are facing while also tying in the fact that it cant be fixed and how this needs to be changed. The childs voice is also clear throughout this speech when she dreams â€Å"of great herds of wild animals, jungles and rainforests full of birds and butterflies† and she uses this to spike thought and emotion from the audience when she states â€Å"I wonder if they will exist for my children to see. Did you ever have to worry abput these little things growing up†. This speech sparks thought and emotion from the audience which is exactly what it needs to do so something will be changed and it promotes a significant environmental aspect of everyones lives aiming for change. A voice is used to challenge, change and inspire audiences and John F. Kennedys Inaugural Address perpetuates this. He acknowledges change, pledges  support, shows acceptance of responsibilities and rallys action and participation from the citizens. He uses a strong presidential voice to portray a view of the America he invisions, â€Å"we shall always hope to find them strongly supporting their own freedom and to remember that, in the past, those who were foolishly sought power by riding the tiger ended up inside† this metaphor highlights his strength and authority to perpetuate to the citizens what they should do and what will happen if they don’t obey or learn from their mistakes. He also uses a religious voice to show his beliefs and inspire more people to follow him, he states â€Å"to undo the heavy burdens and to let the oppressed go free† which is a passeage from the book of Isiah. This passage declares the Christian faith and presidency is underpinned by Christian values. Through the use of metaphor and biblical allusion Kennedy was able to provide light onto his rein as president and show his intentions as president which inspired the individuals of America respond with hope for their country. A distinctive voice is one way that composers connect with their audiences. Gwen Harwood in the poem The Sharpness of Death portrays the idea that life is to be treasured and if you stop and ponder death to often you will waste your life away, you may aswell be dead already, life is for the living through the composers voice which is evident in the use of an oxymoron. â€Å"untranslatable meanings† which shows understanding but in the voice of the character death is seen as inevitable and pain and suffering is normal, she sort of talks to death witnessed when she states â€Å"if I fall from that time then set your teeth in me†. The expresses the thought of her death and she tells death that if she were to forget of the greatest people and times in her life she wants to die for she does not want to experience life without these. Through the two voices provided in one text Harwood was able to portray to visions of living which is an aspect of every individuals life, their demise. They imagine it over and think about it so often and Gwen Harwood aimed to provide an understanding of how she handles the concept of death with the thought of life and how we exist now and our memories are more important. She uses two voices to emphasise the fact that we exist now and how individuals should be reminded of this before they think of death. Unique voices are what stands out when an influential person or significant topic are spoken about, These different angles provide individuals a way of understanding or thinking that would not be usual for them. These voices are able to locate and emphasise aspects of life all individuals will relate to and are able provide solutions or aid the thoughts of the individual. John F. Kennedys Inaugural Address, Severn Cullis-Severn’s address to the plenary session, earth summit and The Sharpness of Death by Gwen Harwood all perpetuate this theme using techniques such as metaphor, anaphora and biblical allusion allowing individuals to reflect and change themselves through inspiration of others.

Principles for Implementing Duty of Care

1. 1. Explain what it means to have duty of care in own work role A duty of care is a legal obligation imposed on an individual requiring that they adhere to a standard of reasonable care. It’s a requirement to exercise a reasonable degree of attention and caution to avoid negligence which could lead to harm to others. Duty of Care is the legal responsibility, to ensure the safety and well-being of others 1. 2. Explain how duty of care contributes to the safeguarding or protection of individuals. Policies and Procedures –are rules set out by your work place the procedures are there to be followed and to safeguard the individuals that we care for Conforming to Legislation-by following legislation such as Health and Social Care Act 2008 Risk Assessments-by following and review risk assessments reporting concerns and reporting potential hazards will minimize any risk Training-to ensure my training is up to date and that I am aware of any changes in legislation 2. 1. Describe Potential Conflicts or Dilemmas That May Arises between the Duty of Care an Individual’s Rights. As individuals we all have our own minds and most of us can do what we want when we want without asking permission, and as we get older our brains do not work as well as it used to . so if an individual was trying to leave the home on their own it would be my duty to try and stop them from leaving as they could put themselves in danger. The individual may not realise how dangerous it could be if she lost her way or even forgets where she lives, we would need to explain the risks if the individual did leave on their own and try and come to some sort of compromise to reduce the risk like the ndividual having an escort . 2. 2. Describe how to manage risks associated with conflicts or dilemmas between an individual`s rights and the duty of care. We would do a risk assessment on the individual and talk to them and hopefully come to compromise with them; we would also ensure all doors are alarmed to alert staff if any doors are opened . all the information will be put into the individuals care plan and all staff would be made aware 2. Explain where to get the additional support and advice about conflicts and dilemmas. Manager Senior carer District nurse Social services 3. 1. Describe how to respond to complaints If a service user or a member of family has a complaint to make I would listen to what they have to say and if I could deal with it myself I would do so if not I would report it to my manager and I would do this effectively and be professional. 3. 2 Explain the main points of agreed procedures for handing complaints. It is important that the home runs smoothly and that staff, clients and relatives work together to benefit the clients In event of complaints from either staff, clients or relatives every effort will be made to respond quickly and appropriately and procedures will be followed most complaints can be handled by care staff, but if we could not deal with it we would inform the senior carer on duty, and if they could not deal with it I would speak to my manager. Every client has the complaint procedure in there room which explains what to do.

Posters Essay Example | Topics and Well Written Essays - 1500 words

Posters - Essay Example Posters are used in almost all types of industries. Companies use them for advertising their products. They consider posters as one of the important way to reach the customers. It is an effective method of advertising, as most of the people who pass by that poster will definitely take a look at it. Posters can be in the form of banner and hoardings. They are put up at important places so that more number of people will observe it. The advertisers adopt various strategies to attract people. They make sure it reaches people of all strata of the society. They should be easier for the people to understand. The information should be in a short and clear manner. It should be concise and correct. As posters draw the attention of large number of people, the advertisers must be careful in selecting the picture and information. They must ensure that it does not convey any wrong data. Posters communicate with people in a better way than any other medium of advertisement. They are not only used for advertisement. In some cases, posters are used for spreading awareness among the public. Poster reaches the public easily and it is one of the best ways to convey information to the people. They are most sought after by the politicians. As they use it for canvassing during the elections, it acts as the best medium. Posters are a boon to film industry. They make use of posters to advertise about the upcoming movies. They put up the pictures of the film stars so that people will be more interested. It helps them in increasing the number of viewers for their film and it includes the necessary information regarding that film. Some posters include obscene pictures to lure the customers. There should be a censorship to control and have a check on this kind of unhealthy publicity by unwanted elements. This will spoil the future society as a whole and also the culture of our country. A poster depicting the Kumbh mela which is a religious function held in the year 2001. This gathering is conducted every 12years. This poster conveys the Indian tradition and integration as people from various parts of the country assemble to take part in this religious activity. Next poster exhibits the Elephant festival. This takes place every year in Kerala during the month of April and May. This shows the tradition of Kerala, where they consider elephant as an incarnation of god. A poster showing the dance form Bharatanatyam. It is the traditional dance form of Tamilnadu and one of the ancient dance forms in India. This type of poster will be displayed in places where such dance programs are held. This poster depicts the Durga Pooja which is held in West Bengal. It is a religious activity that is being performed every year during the month of October. This exhibits the diversity in the culture within India. This poster shows the boat race that took place in Kerala in the year 2002. It is an example of integration among the people of kerala. This is held every year during the festival of onam. The culture is being followed for many decades. As this race is held in a very grand manner, these kind of posters are put up all over Kerala. This poster of traditional bulfight was put up in villages of Tamilnadu during the pongal festival. Generally known as "jallikattu",this form of bulfight is conducted in Tamilnadu and few other parts of Andhra Pradesh. In this, a bag of money