Objectives: To assess general social factors for elderly in geriatric homes in Jordan.
Methodology: The study was conducted for clients residing in elderly homes. A purposive sample of (155)
residents were selected. Social resource scale by (Duck University Center) called (OARA) older adult resources
and services was used as tool for data collection. The questions extract data about family structure, patterns of
friends and visiting, availability of a confident, satisfaction with the degree of social interaction and availability
of a helper in the event of illness or disability and the program of trips in the houses.
Results: The findings revealed that the life expectancy for women is greater than for men. In respect to marital
status, the majority of the residents were widows who represented (38.I). Only ( 15.5%) of them were visited by
their family more than (5) tines in the year. The study shows that (61.3%) of them have trust to the home's staff
and (25.1%) to their friends in the home. In regard to loneliness, (66.5%) of them feels loneliness and they need
outside assistance, In regard to clients' help, (42%) of them could not know or answer this question. While,
(51.6%) of them need help from inside on bathing, eating or taking medication. In relation to clients trips outside
home, (54.8%) of them have the chance to go outside the home for trip; mainly for visiting family, friends or for
religious purposes.
Recommendation: The study recommended that the leaders of the homes must focus on increasing the facilities
of social relation through more communication, increase visiting times between the elderly and their families
through the social worker of the house, get out of the house and meet other people, invite people home, an.ange
regular tines for getting together with fiiends and family members, and find activities that involve doing things.
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Social media is known as detectors platform that are used to measure the activities of the users in the real world. However, the huge and unfiltered feed of messages posted on social media trigger social warnings, particularly when these messages contain hate speech towards specific individual or community. The negative effect of these messages on individuals or the society at large is of great concern to governments and non-governmental organizations. Word clouds provide a simple and efficient means of visually transferring the most common words from text documents. This research aims to develop a word cloud model based on hateful words on online social media environment such as Google News. Several steps are involved including data acq
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