How large is the dataset?
70287
For the webportal"Who is in the News!"with statistics about the appearence of persons in written news we developed an extension, which measures the relationship of public persons depending on a time parameter, as the relationship may vary over time. On a training corpus of English and German news articles we built a measure by extracting the persons occurrence in the text via pretrained named entity extraction and then construct time series of counts for each person. Pearson correlation over a sliding window is then used to measure the relation of two persons.
“Who is in the News!” is a webportal with statistics and plots about the appearence of persons in written news articles. It counts how often public persons are mentioned in news articles and can be used for research or journalistic purposes. The application is indexing articles published by “Reuters” agency on their website . With the interactive charts users can analyze different timespans for the mentiones of public people and look for patterns in the data. The portal is bulit with the Python microframework “Dash" which uses the plattform “Plotly" for the interactive charts. Playing around with the charts shows some interresting patterns like the one in the example of Figure FIGREF5 . This figure suggests that there must be some relationship between this two persons. In this example it is obvious because the persons are both german politicians and candidates for the elections. This motivated us to look for suitalbe measures to caputure how persons are related to each other, which then can be used to exted the webportal with charts showing the person to person relationships. Relationship and distance between persons have been analyzed for decades, for example BIBREF0 looked at distance in the famous experimental study “the Small World Problem”. They inspected the graph of relationships between different persons and set the “distance” to the shortest path between them. Other approaches used large text corpora for trying to find connections and relatedness by making statistics over the words in the texts. This of course only works for people appearing in the texts and we will discuss this in section SECREF2 . All these methods do not cover the changes of relations of the persons over time, that may change over the years. Therefore the measure should have a time parameter, which can be set to the desired time we are investigating. We have developed a method for such a measure and tested it on a set of news articles for the United States and Germany. In Figure FIGREF6 you see how the relation changes in an example of the German chancellor ”Angela Merkel” and her opponent on the last elections “Martin Schulz”. It starts around 0 in 2015 and goes up to about 0.75 in 2017 as we can expect looking at the high correlated time series chart in Figure FIGREF5 from the end of 2017.