Visualization resonance for collaborative discourse

1. A computer-implemented method for collaborative discourse, the computer-implemented method comprising:

responsive to receiving a collaborative discourse, analyzing, by one or more computer processors, a document corpora of the collaborative discourse to determine a set of content in and a target audience of the document corpora of the collaborative discourse;

analyzing, by the one or more computer processors, a picture metadata for each image of one or more images in a graphic repository to determine whether each image of the one or more images in the graphic repository align with a set of tagged image metadata;

deriving, by the one or more computer processors, a machine learning model from a result of the analysis of the document corpora and the analysis of the picture metadata;

selecting, by the one or more computer processors, one or more appropriate images of the one or more images in the graphic repository based on the machine learning model, wherein the one or more appropriate images align with the collaborative discourse, and wherein the one or more appropriate images can be embodied as a composition plugin to the collaborative discourse to increase user attention to the set of content;

applying, by the one or more computer processors, the one or more appropriate images selected to the collaborative discourse; and

responsive to receiving a feedback from a user on the one or more appropriate images, refining, by the one or more computer processors, the machine learning model by applying a Human in the Loop analysis of additional viewing measurements.

2. The computer-implemented method of claim 1 , wherein

the document corpora of the collaborative discourse is analyzed using at least one of one or more topic modeling techniques, one or more corpus linguistic methods, and one or more readability indices.

3. The computer-implemented method of claim 1 , wherein selecting the one or more appropriate images of the one or more images in the graphic repository based on the machine learning model further comprises:

determining, by the one or more computer processors, whether the one or more appropriate images of the one or more images in the graphic repository that align to the collaborative discourse can be embedded into the collaborative discourse; and

responsive to determining that the one or more appropriate images of the one or more images in the graphic repository that align to the collaborative discourse can be embedded into the collaborative discourse, embedding, by the one or more computer processors, the one or more appropriate images into the collaborative discourse.

4. The computer-implemented method of claim 1 , further comprising:

determining, by the one or more computer processors, whether the collaborative discourse contains a sensitive information;

responsive to determining that the collaborative discourse contains the sensitive information, selecting, by the one or more computer processors, one or more alert images of the one or more images in the graphic repository based on the picture metadata, wherein the picture metadata indicates the one or more alert images are associated with previous alerts; and

embedding, by the one or more computer processors, the one or more alert images into the collaborative discourse.

5. The computer-implemented method of claim 1 , wherein the collaborative discourse includes at least one of a wiki, a blog, and an article.

6. A computer program product for collaborative discourse, the computer program product comprising one or more computer readable storage media and program instructions stored on the one or more computer readable storage media, the program instructions including instructions to:

responsive to receiving a collaborative discourse, analyze a document corpora of the collaborative discourse to determine a set of content in and a target audience of the document corpora of the collaborative discourse;

analyze a picture metadata for each image of one or more images in a graphic repository to determine whether each image of the one or more images in the graphic repository align with a set of tagged image metadata;

derive a machine learning model from a result of the analysis of the document corpora and the analysis of the picture metadata;

select one or more appropriate images of the one or more images in the graphic repository based on the machine learning model, wherein the one or more appropriate images align with the collaborative discourse, and wherein the one or more appropriate images can be embodied as a composition plugin to the collaborative discourse to increase user attention to the set of content;

apply the one or more appropriate images selected to the collaborative discourse; and

responsive to receiving a feedback from a user on the one or more appropriate images, refine the machine learning model by applying a Human in the Loop analysis of additional viewing measurements.

7. The computer program product of claim 6 , wherein

the document corpora of the collaborative discourse is analyzed using at least one of one or more topic modeling techniques, one or more corpus linguistic methods, and one or more readability indices.

8. The computer program product of claim 6 , wherein selecting the one or more appropriate images of the one or more images in the graphic repository based on the machine learning model further comprises one or more of the following program instructions, stored on the one or more computer readable storage media, to:

determine whether the one or more appropriate images of the one or more images in the graphic repository that align to the collaborative discourse can be embedded into the collaborative discourse; and

responsive to determining that the one or more appropriate images of the one or more images in the graphic repository that align to the collaborative discourse can be embedded into the collaborative discourse, embed the one or more appropriate images into the collaborative discourse.

9. The computer program product of claim 6 , further comprising one or more of the following program instructions, stored on the one or more computer readable storage media, to:

determine whether the collaborative discourse contains a sensitive information;

responsive to determining that the collaborative discourse contains the sensitive information, select one or more alert images of the one or more images in the graphic repository based on the picture metadata, wherein the picture metadata indicates the one or more alert images are associated with previous alerts; and

embed the one or more alert images into the collaborative discourse.

10. The computer program product of claim 6 , wherein the collaborative discourse includes at least one of a wiki, a blog, and an article.

11. A computer system for collaborative discourse, the computer system comprising:

one or more computer processors;

one or more computer readable storage media; and

program instructions stored on the one or more computer readable storage media for execution by at least one of the one or more computer processors, the stored program instructions including instructions to:

responsive to receiving a collaborative discourse, analyze a document corpora of the collaborative discourse to determine a set of content in and a target audience of the document corpora of the collaborative discourse;

analyze a picture metadata for each image of one or more images in a graphic repository to determine whether each image of the one or more images in the graphic repository align with a set of tagged image metadata;

derive a machine learning model from a result of the analysis of the document corpora and the analysis of the picture metadata;

select one or more appropriate images of the one or more images in the graphic repository based on the machine learning model, wherein the one or more appropriate images align with the collaborative discourse, and wherein the one or more appropriate images can be embodied as a composition plugin to the collaborative discourse to increase user attention to the set of content;

apply the one or more appropriate images selected to the collaborative discourse; and

responsive to receiving a feedback from a user on the one or more appropriate images, refine the machine learning model by applying a Human in the Loop analysis of additional viewing measurements.

12. The computer system of claim 11 , wherein

the document corpora of the collaborative discourse is analyzed using at least one of one or more topic modeling techniques, one or more corpus linguistic methods, and one or more readability indices.

13. The computer system of claim 11 , wherein selecting the one or more appropriate images of the one or more images in the graphic repository based on the machine learning model further comprises one or more of the following program instructions, stored on the one or more computer readable storage media, to:

determine whether the one or more appropriate images of the one or more images in the graphic repository that align to the collaborative discourse can be embedded into the collaborative discourse; and

responsive to determining that the one or more appropriate images of the one or more images in the graphic repository that align to the collaborative discourse can be embedded into the collaborative discourse, embed the one or more appropriate images into the collaborative discourse.

14. The computer system of claim 11 , further comprising one or more of the following program instructions, stored on the one or more computer readable storage media, to:

determine whether the collaborative discourse contains a sensitive information;

responsive to determining that the collaborative discourse contains the sensitive information, select one or more alert images of the one or more images in the graphic repository based on the picture metadata, wherein the picture metadata indicates the one or more alert images are associated with previous alerts; and

embed the one or more alert images into the collaborative discourse.

15. The computer system of claim 11 , wherein the collaborative discourse includes at least one of a wiki, a blog, and an article.