Audience Metrics in Media Organizations

Audience metrics is becoming widely used in many media organizations but there is little research on how it impacts the daily works of journalists and newsroom culture as well as how it is produced. In her article Caitlin Pere, provides a research on the role of metrics in journalism on cases studies of Gawker Media, Chart beat and The New York Times. Based on her findings she provides five key points. One of the major key points is that metrics exert powerful impacts on the morale and emotions of journalists. Metrics tend to trigger several feelings in journalists including anxiety, excitement, self-doubt, competition, triumph and demoralization (Petre, 2015). Thus, newsroom managers need to consider the significant impacts of traffic data on editorial workers and editorial content when they devise internal policies for metrics usage.

Traffic based ranks can overpower other evaluation forms. It is common for many journalists to have fixed interests on metrics ranking them along with their stories even when they are not always evaluated through this criterion. When rankings become prominent on the websites and walls of newsrooms, it will become challenging to limit their impacts. Also new media houses can significantly benefit from strategic thing and big picture concerning analytics. Many journalists are often too busy with their day to day activities to abstractly and extensively think about the role of metrics in their organizations. They also fail to realize which metrics can effectively align with their journalistic goals. Due to this, they tend to interprets, consult and employ metrics in a way that is ad hoc. However, this data may become too powerful for implementation on the fly. News media houses need to come up with opportunities by pioneering with other investigators (Christin, 2018). This is because it enables deliberate and reflective thinking to be eradicated from day to day production pressures concerning how they can effectively use analytics.

In her article,   Christin analyses the significant meanings and uses of audience metrics in France and United States. According to the writer, newsrooms contemporary depend on different software programs that in real time produce detailed data concerning the online popularity of articles (Christin, 2018). The use of analytics has significantly lead to improvements from the print newspapers situations. When media organizations choose analytics services they have to consider the vendors’ values and business models used. People have the tendency of seeing numbers and by analytics extension dashboards as dispassionate and authoritative reflections of the empirical world. However, when they choose an analytics service, it is critical for them to understand that analytics organizations have their imperatives of business. Newsroom managers need to consider branding strategies, values, and strategic objectives of a company align with their goals. Nonetheless, not everything needs to be counted. The efforts of measuring news impacts and for improving audience analytics are worthwhile and critical. However, analytics companies, newsrooms, media researchers and funders need to consider some sceptic indispensable and compelling traits in journalism like its social mission are not measured easily (Petre, 2015). Considering that data analytics are contemporarily increasingly volarized, people must be aware not equate what is valuable and what is quantifiable.

Even through metrics is viewed to change journalism, new research shows that journalism is changing metrics. For instance, Gawker’s practices and norms are significantly influenced by the historical emphasis of the company on metrics (Petre, 2015). However, The New York Times has a longstanding organizational structure and culture that shapes newsroom’s metrics role. Journalism also has various goals that can make interpretation of metrics difficult. During their interpretation of metrics some journalists may find it challenging to find out what leads to appropriate standard and fair comparison of an article. Metrics also lead to significant stress on editors and writers. Therefore. Metrics play a significant role in shaping newsroom organizations and vice versa.

Predictive Policing Systems in Law Enforcements

Current law enforcements are increasingly using predictive policing systems to to prevent crime. However, these systems may be trained using biased information leading to significant consequences. In their article Lum and Isaac provide examples of how predictive policing are biased and their social consequences. According to the authors, predictive policing software’s including their policing techniques have raised significant concerns among legal scholars, community activists and skeptical police chiefs (Lum & Isaac, 2016). Some of the major concerns include: the lack of transparency from private firms and police departments concerning the construction of predictive policing techniques; the concept and seizure of reasonable suspicion and conflicts unlawful search protections; the utilization data in departments; and if the programs are biased by targeting some people more than others.

There is a major concern that record data sets in police departments are made of systematic bias. Predictive policing software’s are created to reproduces and learn patterns. However, when biased data are used to train the predictive models, these models are likely to reproduce and lead to amplification of those biases. This may render the predictive models ineffective and lead to discriminatory policing. According to research, police databases have failed to be a complete census of many criminal offences (Lum & Isaac, 2016). They also do not constitute a representative random sample. Furthermore, police officers explicitly or implicitly consider ethnicity and race when determining who should have searched and detained as well as which neighborhood need to be patrolled. This indicates that it is high likely that police records may systematically over-represent certain neighborhood and groups of people in their criminal records.

In their research Lum and Isac find out that predictive policing of drug crimes leads to significant disproportionate policing of communities that have historically been over-policed (Lum & Isaac, 2016). These communities have experienced real costs as a result of over-policing. The increasing police surveillance and scrutiny on these communities is connected to problems of physical and mental health. Nonetheless, additional and extreme police contact is likely to lead to more opportunities for police brutality on over-policed communities. The biased policing strategies have also been significantly contributed by sophisticated software’s that are only understood by few people. There is a significant need to understand the impacts of predictive policing software’s not only on the society but also the reputation of police departments.

References

Christin, A. (2018). Counting clicks: Quantification and variation in web journalism in the United States and France. American Journal of Sociology, 123(5), 1382-1415.

Petre, C. (2015, May). The Traffic Factories: Metrics at Chartbeat, Gawker Media, and The New York Times. Columbia Journalism Review. https://www.cjr.org/tow_center_reports/the_traffic_factories_metrics_at_chartbeat_gawker_media_and_the_new_york_times.php

Lum, K., & Isaac, W. (2016, October 1). Royal Statistical Society Publications. Royal Statistical Society. https://rss.onlinelibrary.wiley.com/doi/full/10.1111/j.1740-9713.2016.00960.x