25 Things We Learned Analyzing Billions of Tweets
6,000 Tweets are posted every second. That’s 6,000 views, opinions, ideas, complaints, stories, compliments. It’s live human thought. And we can use it to try and answer any question.
Source: 25 Things We Learned Analyzing Billions of Tweets | Brandwatch
A global median of 75% want their news media to be unbiased when covering political issues, yet many say the news media do a poor job of reporting on political issues fairly.
| Political issues fairly | News about govt. leaders and officials |
News accurately | Most important news events | |
| Greece | 18% | 25% | 22% | 42% |
| South Korea | 27% | 26% | 36% | 44% |
| Spain | 33% | 48% | 48% | 63% |
| Chile | 36% | 36% | 42% | 55% |
| Italy | 36% | 46% | 45% | 60% |
| Lebanon | 36% | 62% | 48% | 73% |
| Argentina | 37% | 38% | 45% | 56% |
| Colombia | 38% | 43% | 41% | 57% |
| Poland | 40% | 43% | 43% | 59% |
| Peru | 42% | 42% | 51% | 59% |
| Hungary | 42% | 49% | 54% | 63% |
| Israel | 42% | 50% | 63% | 78% |
Source: People Around World Want Unbiased News | Pew Research Center
The aim was to identify hate speech targeted at minorities and people in a vulnerable position.
During the municipal elections in spring 2017, a group of researchers and practitioners specialising in computer science, media and communication implemented a hate speech identification campaign with the help of an algorithm based on machine learning.
At the beginning of the campaign, the algorithm was taught to identify hate speech as diversely as possible, for example, based on the big data obtained from open chat groups. The algorithm learned to compare computationally what distinguishes a text that includes hate speech from a text that is not hate speech and to develop a categorisation system for hate speech. The algorithm was then used daily to screen all openly available content the candidates standing in the municipal elections had produced on Facebook and Twitter. The candidates’ account information were gathered using the material in the election machine of the Finnish Broadcasting Company Yle.
Source: News: Internet researchers harnessed the power of algorithm to find hate speech – Aalto University
A study casts a light not just on gender bias, but also on how algorithms work.
The tennis finals of the United States Open are Saturday for the women and Sunday for the men. On the court, except for the number of sets, they all face the same rules. When they walk off the court, though, the game changes.
In this paper, we study the linking patterns and discussion topics of political bloggers. Our aim is to measure the degree of interaction between liberal and conservative blogs, and to uncover any differences in the structure of the two communities. Specifically, we analyze the posts of 40 “A-list” blogs over the period of two months preceding the U.S. Presidential Election of 2004, to study how often they referred to one another and to quantify the overlap in the topics they discussed, both within the liberal and conservative communities, and also across communities. We also study a single day snapshot of over 1,000 political blogs. This snapshot captures blogrolls (the list of links to other blogs frequently found in sidebars), and presents a more static picture of a broader blogosphere. Most significantly, we find differences in the behavior of liberal and conservative blogs, with conservative blogs linking to each other more frequently and in a denser pattern.
