媒体型智库舆论引导力影响因素实证研究

Empirical Research on the Influencing Factors of Public Opinion Guidance of Media-Based Think Tanks

【目的/意义】媒体型智库是智库与媒体融合的最新态,在舆论引导方面具有天然优势。探明媒体型智库舆论引导力的影响因素及其相对重要性可为针对性提升业界媒体型智库舆论引导力提供理论支撑。【方法/过程】首先建构媒体型智库舆论引导力影响因素体系,然后通过问卷采集其舆论引导力及分力与影响因素相关数据,再使用机器学习方法(选用XGBoost算法)建立预测模型,最后通过计算增益得到影响因素的相对重要性。【结果/结论】研究发现,媒体型智库舆论引导力及其分力受主体、客体、媒介及场域的多个维度因素影响。主体维度因素对媒体型智库舆论引导力的整体影响最大,场域维度次之。舆论引导者协同合作程度等是影响媒体型智库舆论引导力的关键因素。【创新/局限】本文创新性提出媒体型智库舆论引导力影响因素体系,并采用机器学习方法通过建立预测模型求解了影响因素相对重要性。扩充调研数据、尝试其他算法是进一步提升方向。

【Purpose/significance】Media-based think tanks is the latest state of the integration of think tanks and media,and have natural advantages in guiding public opinion. Exploring the influencing factors and its relative importance of the public opinion guidance of media-based think tanks will provide theoretical support for the targeted construction of media-based think tank’s public opinion guidance.【Method/process】Firstly,this paper puts forward the influencing factor system of public opinion guidance of mediabased think tanks;Then,the data of influencing factors,public opinion guidance and its component forces are collected through the questionnaire,and then the prediction model is established by using machine learning method(XGBoost algorithm). Finally,the relative importance of the influencing factors is obtained by calculating the gain.【Result/conclusion】It is found that the public opinion guidance and its component forces of media-based think tanks are affected by multiple factors of subject,object,media and field dimension.Subject dimension factors have the greatest impact on the public opinion guidance of media-based think tanks,followed by the field dimension. The degree of coordination and cooperation of public opinion guides is the key factor affecting the public opinion guidance of media-based think tanks.【Innovation/limitation】This paper innovatively puts forward the influencing factor system of public opinion guidance of media-based think tanks,and uses machine learning method to solve the relative importance of influencing factors by establishing a prediction model. Expanding data and trying other algorithms are the direction of further improvement.