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Rapid developments in social networks and their usage in everyday life have caused an explosion in the amount of short electronic documents. Thus, the need to classify this type of document based on their content has a significant implication in many applications. The need to classify these documents in relevant classes according to their text contents should be interested in many practical reasons. Short-text classification is an essential step in many applications, such as spam filtering, sentiment analysis, Twitter personalization, customer review and many other applications related to social networks. Reviews on short text and its application are limited. Thus, this paper aims to discuss the characteristics of short text, its challenges and difficulties in classification. The paper attempt to introduce all stages in principle classification, the technique used in each stage and the possible development trend in each stage.

The paper as a review of the main aspect of short-text classification. The paper is structured based on the classification task stage.

This paper discusses related issues and approaches to these problems. Further research could be conducted to address the challenges in short texts and avoid poor accuracy in classification. Problems in low performance can be solved by using optimized solutions, such as genetic algorithms that are powerful in enhancing the quality of selected features. Soft computing solution has a fuzzy logic that makes short-text problems a promising area of research.

Using a powerful short-text classification method significantly affects many applications in terms of efficiency enhancement. Current solutions still have low performance, implying the need for improvement. This paper discusses related issues and approaches to these problems.

This paper aims to examine the perceived influence of knowledge management (KM) practices on the success of e-government initiatives. This paper proposes a framework depicting the overall perspective of the interactions between the environment and KM practices and associated processes in the context of e-government.

A conceptual framework was built to set the stage for empirical analysis, which included four major constituents: IT infrastructure, administrative issues, KM practices and e-government projects success. A sample of 181 civil servants completed a survey measuring the factors included in the research model. Structural equation modeling technique was used to test the model.

Results have identified IT infrastructure and administrative issues as significant predictors of e-government projects’ success, where the relationship was mediated by KM practices. The model explained 52.7 per cent of the variance in e-government success.

Governments need to enforce policies to encourage KM practices and make available the needed infrastructure for such environment. The sample size and the new Arabic survey used in the study are the major limitations, where more research is encouraged to validate the instrument and generalize the findings to different environments.

This study is the first in Jordan, and one of the few that related e-government to KM practices by proposing a comprehensive model that sums the factors related to such relationship. Its value stems from its sample of public employees and the support of its proposed framework.