The research is aimed at investigating how the New York Times framed the war against ISIS in its news coverage and which news sources it adopted while reporting on this war.
The research could be classified under descriptive researches. The survey methodology has been adopted and the content analysis has been used. The research sample consists of all the news stories the New York Times have published about the war against ISIS from 10/17/2016 to 4/16/2017 according to the comprehensive sampling method. The number of news stories that were analyzed was (155) news story. The research tool was (coding scheme).
The research has reached the following conclusions:
1. In its news coverage of the war against ISIS, the New York Times focused on highlighting the US role in the war, amplifying it and presenting it as major and pivotal.
2. The newspaper did not pay any attention to counter-frames, that is, the frames that contradict the U.S. participation in the war against ISIS, as it did not publish any news story that present opinions opposing the participation of the U.S. in this war.
3. The newspaper framed the war against ISIS within the war on terror frame, by indicating the continuity of the threat posed by ISIS to international security and peace.
4. The newspaper preferred the adoption of official news sources at the expense of informal news sources.
5. The research revealed the newspaper s bias to American official news sources, and to news sources that have good relationships with American government at the expense of news sources that do not have such relationships.
6. A drop in the use of anonymous sources has been observed that agrees with the results of some recently conducted researches.
Everybody is connected with social media like (Facebook, Twitter, LinkedIn, Instagram…etc.) that generate a large quantity of data and which traditional applications are inadequate to process. Social media are regarded as an important platform for sharing information, opinion, and knowledge of many subscribers. These basic media attribute Big data also to many issues, such as data collection, storage, moving, updating, reviewing, posting, scanning, visualization, Data protection, etc. To deal with all these problems, this is a need for an adequate system that not just prepares the details, but also provides meaningful analysis to take advantage of the difficult situations, relevant to business, proper decision, Health, social media, sc
... Show MoreAfter the year 2003, Iraq went through multiple waves of violence and at different levels on the security, intellectual, political and social levels. Behind that stood several motives and incentives to enable violence that represented the first axis of research, the most important of which was the political motives that circulated an atmosphere that politics against society and transformed power into a field of political brutality against the individual and the group at once. There are also cultural, intellectual, media and economic motives such as weak cultural independence, poverty, marginalization, unemployment and want, and the absence of a media discourse that rejects violence but incites it, on the other ha
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A Ligand (ECA) methyl 2-((1-cyano-2-ethoxy-2-oxoethyl)diazenyl)benzoate with metals of (Co2+, Ni2+, Cu2+) were prepared and characterization using H-NMR, atomic absorption spectroscopy, ultra violet (UV) visible, magnetic moments measurements, bioactivity, and Molar conductivity measurements in soluble ethanol. Complexes have been prepared using a general formula which was suggested as [M (ECA)2] Cl2, where M = (Cobalt(II), Nickel(II) and Copper(II), the geometry shape of the complexes is octahedral.
The prediction process of time series for some time-related phenomena, in particular, the autoregressive integrated moving average(ARIMA) models is one of the important topics in the theory of time series analysis in the applied statistics. Perhaps its importance lies in the basic stages in analyzing of the structure or modeling and the conditions that must be provided in the stochastic process. This paper deals with two methods of predicting the first was a special case of autoregressive integrated moving average which is ARIMA (0,1,1) if the value of the parameter equal to zero, then it is called Random Walk model, the second was the exponential weighted moving average (EWMA). It was implemented in the data of the monthly traff
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