THE NEWS SHARING GAP: how political context helps to explain news routines and shareworthiness on SNSs

LYCARIÃO, DIÓGENES; SANTOS, MARCELO ALVES DOS; LEITE, ANA BEATRIZ

doi:10.25200/BJR.v17n3.2021.1453

DIÓGENES LYCARIÃO

Universidade Federal do Ceará, Fortaleza – Ceará – Brazil. E-mail: lycariao-d@ufc.br

DIÓGENES LYCARIÃO. Obtained his Ph.D. at Federal University of Minas Gerais (UFMG), in 2014. He is an assistant professor in the Graduate Program in Communication, Federal University of Ceará (PPGCOM/UFC). Collaboration in this article: research design concept, literature review, content analysis of news articles, data discussion, writing and editing of the text and manuscript’s final approval. E-mail: lycariao-d@ufc.br

http://orcid.org/0000-0002-8924-7442

MARCELO ALVES DOS SANTOS

Pontifícia Universidade Católica do Rio de Janeiro – Rio de Janeiro – Rio de Janeiro – Brazil. E-mail: malvesjor@gmail.com

MARCELO ALVES DOS SANTOS. Obtained his Ph.D. at Fluminense Federal University (UFF), in 2019. He is an assistant professor in the Graduate Program in Communication, Pontifical Catholic University of Rio de Janeiro (PPGCOM/PUC-Rio) His research is in the field of internet and politics, focusing on elections, party system, public opinion, and social media. Collaboration in this article: data collection, statistical analyses, data visualization, data discussion, proofreading support, writing, text editing, and manuscript’s final approval. E-mail: malvesjor@gmail.com

http://orcid.org/0000-0003-4995-6612

ANA BEATRIZ LEITE

Universidade Federal do Ceará, Fortaleza – Ceará – Brazil. E-mail: anabmilk@gmail.com

ANA BEATRIZ LEITE. She obtained her Master’s degree at the PPGCOM/UFC, in 2021. Her research is in the field of journalism studies. Collaboration in this article: content analysis of news articles, codebook improvement, literature review support, data discussion, writing, text editing, and manuscript’s final approval. E-mail: anabmilk@gmail.com

http://orcid.org/0000-0001-8551-3232

Desk Review Editor: Lia Seixas

DIÓGENES LYCARIÃO. Obtained his Ph.D. at Federal University of Minas Gerais (UFMG), in 2014. He is an assistant professor in the Graduate Program in Communication, Federal University of Ceará (PPGCOM/UFC). Collaboration in this article: research design concept, literature review, content analysis of news articles, data discussion, writing and editing of the text and manuscript’s final approval. E-mail: lycariao-d@ufc.br

MARCELO ALVES DOS SANTOS. Obtained his Ph.D. at Fluminense Federal University (UFF), in 2019. He is an assistant professor in the Graduate Program in Communication, Pontifical Catholic University of Rio de Janeiro (PPGCOM/PUC-Rio) His research is in the field of internet and politics, focusing on elections, party system, public opinion, and social media. Collaboration in this article: data collection, statistical analyses, data visualization, data discussion, proofreading support, writing, text editing, and manuscript’s final approval. E-mail: malvesjor@gmail.com

ANA BEATRIZ LEITE. She obtained her Master’s degree at the PPGCOM/UFC, in 2021. Her research is in the field of journalism studies. Collaboration in this article: content analysis of news articles, codebook improvement, literature review support, data discussion, writing, text editing, and manuscript’s final approval. E-mail: anabmilk@gmail.com

	Dependent variable
	Log shares
	Brazil (1)	Canada (2)	US (3)	All (4)
Control variables

Fox News				3.489^* ***** p<0.01
				(0.172)
G1				1.147^* ***** p<0.01
				(0.152)
Veja	-0.305^* * p<0.1;			0.875***
	(0.159)			(0.141)
Global News		-1.649^* ***** p<0.01		-1.640^* ***** p<0.01
		(0.116)		(0.121)
The New York
Times			-2.730^* ***** p<0.01	0.925^* ***** p<0.01
			(0.170)	(0.142)
Native video	-0.039	2.562^* ***** p<0.01	0.456^ p<0.05;	1.381^* ***** p<0.01
	(0.407)	(0.204)	(0.209)	(0.144)
Bait video	-0.780^ p<0.05;	-0.437	1.137^ p<0.05;	-0.423^ p<0.05;
	(0.314)	(0.301)	(0.556)	(0.204)
R² Incremental	0.19%	31.89%	45.77%	48.04%

Content variables

Hard news	0.357	1.269^* ***** p<0.01	0.211	0.483^* ***** p<0.01
	(0.362)	(0.406)	(0.187)	(0.157)
Sensational news	0.960^ p<0.05;	1.625^* ***** p<0.01	0.111	0.876^* ***** p<0.01
	(0.388)	(0.417)	(0.262)	(0.174)
Soft news	0.260	1.376^* ***** p<0.01	-0.255	0.407^ p<0.05;
	(0.384)	(0.418)	(0.227)	(0.171)
Political scandal	-0.259	-0.786^ p<0.05;	0.833^* ***** p<0.01	-0.041
	(0.222)	(0.320)	(0.286)	(0.143)
National Politics / Conflict	0.800^* ***** p<0.01	-0.125	-0.209	0.152
	(0.241)	(0.186)	(0.200)	(0.122)
R² Incremental	4.45%	2.59%	4.23%	2.78%
Constant	4.825^* ***** p<0.01	2.805^* ***** p<0.01	7.740^* ***** p<0.01	3.634^* ***** p<0.01
	(0.372)	(0.399)	(0.228)	(0.180)
Observations	488	777	393	1,658
R²	0.064	0.345	0.524	0.508
Adjusted R²	0.048	0.338	0.515	0.505
Residual Std. Error	1.671 (df = 479)	1.460 (df = 768)	1.414 (df = 384)	1.561 (df = 1645)
F Statistic	4.075^* ***** p<0.01 (df = 8; 479)	50.551^* ***** p<0.01 (df = 8; 768)	52.943^* ***** p<0.01 (df = 8; 384)	141.734^* ***** p<0.01 (df = 12; 1645)

Content variable	Operationalization
Post format	Posts were coded in three possible formats: (1) photo/link (the post has a link, with or without a photo, to the news webpage); (2) bait video (the post warns a video is present in the news article); or, (3) native video (an embedded video). (Kα = 0.83; Pa = 98%).
News type	Four types of news posts were coded: (1) hard news (public affairs frame, useful information, or politically relevant events); (2) sensational news (dramatic events framed mainly at the individual level); (3) soft news (funny or curious events framed mainly at the individual level); (4) opinion (posts corresponding to opinion-oriented articles). (Kα = 0.69; Pa = 80%).
Political scandal	Dummy variable identifying whether or not the headline mentions events framed as political scandals. This includes judicial prosecutions of public authorities and leaks involving sensitive material that damages their public image. (Kα = 0.69; Pa = 94%).
Conflict / Controversy	Ordinal variable establishing the level of controversy/conflict depicted by the news post headline. Three levels were identified: (0) Absent; (1) Low (disagreements, controversial topics, political contends); (2) High (protests, judicial actions, violent actions). (Kα = 0.68; Pa = 75%).
Influence	Ordinal variable establishing the level of the political influence of the most powerful person or institution mentioned on the headline. Four levels were identified: (0) Absent; (1) Low (institutions or public authorities at the local level); (2) Intermediate (institutions or public authorities at the national level); (3) High (institutions or public authorities at the international level). (Kα = 0.81; Pa = 85%).
Proximity	Ordinal variable establishing the level of proximity of the central event, person, or institution depicted in the news post. Four levels were identified: (0) Absent/Not Discernible; (1) Low (event/person/institution related to a foreign country from Global South); (2) Intermediate (event/person/institution related to a foreign country member of NATO or OCDE); (3) High (event/person/institution from the same country as the news media outlet). (Kα = 0.86; Pa = 88%).
National politics with conflict / Controversy	This variable identifies news articles by assessing information of three of the aforementioned variables: proximity, influence, and conflict/controversy. This variable was automatically coded as present (1) when the news post was coded with high proximity, any positive level of influence (low, intermediate, or high), and any positive level of conflict/controversy (low or high). This variable was automatically coded as absent (0) when any of these three conditions was not fulfilled.

	News page	Total posts n (%)	Log shares (%)	SD (log shares)	Mean (log shares/post)
Brazil	G1	202 (41%)	43%	1.577	5.370
Brazil	Veja	286 (59%)	57%	1.797	5.0987
Canada	CBC News	252 (32%)	43%	1.671	4.273
Canada	Global News	525 (68%)	57%	1.617	2.690
US	Fox News	140 (36%)	46.7%	1.467	8.057
US	NYT	253 (64%)	53.3%	1.430	5.078

	Post format	Total posts n (%)	Log shares (%)	SD (log shares)	Mean (log shares/post)
Brazil	Native video	20 (4.1%)	4.0%	1.434	5.149
	Picture/link	437 (89.5%)	90.5%	1.732	5.264
	Bait video	31 (6.4%)	5.5%	1.471	4.495
Canada	Native video	56 (7.2%)	13%	1.543	5.692
	Picture/link	696 (89.6%)	85%	1.673	3.033
	Bait video	25 (3.2%)	2%	1.241	2.353
US	Native video	66 (16.8%)	20%	2.329	7.454
	Picture/link	320 (81.4%)	77%	1.806	5.809
	Bait video	7 (1.8%)	3%	1.835	8.853

	Post type	Total posts n (%)	Chi-Square Residuals	Log shares (%)	SD (log shares)	Mean (log shares/post)
Brazil	Hard news	228 (46.7%)	0.50	47.3%	1.649	5.271
	Opinion	27 (5.5%)	-1.90	5.1%	2.357	4.832
	Sensational news	111 (22.7%)	-0.72	24.4%	1.667	5.583
	Soft news	122 (25.0%)	1.16	23.2%	1.638	4.843
Canada	Hard news	361 (46.5%)	0.53	43.9%	1.704	3.023
	Opinion	14 (1.8%)	-6.08*	1.5%	1.377	2.713
	Sensational news	237 (30.5%)	3.46*	31.9%	1.865	3.353
	Soft news	165 (21.2%)	-0.74	22.7%	1.878	3.422
US	Hard news	160 (40.7%)	-1.31	42.2%	1.966	6.371
	Opinion	91 (23.2%)	10.67*	22.8%	2.128	6.052
	Sensational news	56 (14.2%)	-4.06*	15.7%	1.975	6.771
	Soft news	86 (21.9%)	-0.25	19.2%	1.870	5.390

	Political Scandal	Total Posts n (%)	Chi-Square Residuals	Log shares (%)	SD (log shares)	Mean (log shares/post)
Brazil	Absent	352 (72.1%)	-3.87*	72%	1.748	5.201
Brazil	Present	136 (27.9%)	10.84*	28%	1.622	5.236
Canada	Absent	755 (97.2%)	2.51	98%	1.788	3.242
Canada	Present	22 (2.8%)	-7.04*	2%	1.488	1.870
US	Absent	363 (92.4%)	0.78	90%	2.004	6.007
US	Present	30 (7.6%)	-2.18*	10%	1.628	7.740

	National politics (conflict)	Total posts n (%)	Chi-Square Residuals	Log shares (%)	SD (log shares)	Mean (log shares/post)
Brazil	Absent	343 (70%)	-2.58*	69%	1.682	5.095
Brazil	Present	145 (30%)	5.31*	31%	1.758	5.485
Canada	Absent	698 (90%)	2.79*	91%	1.815	3.237
Canada	Present	79 (10%)	-5.73*	9%	1.582	2.907
US	Absent	299 (76%)	-1.04	73%	1.967	5.894
US	Present	94 (24%)	2.14*	27%	2.037	6.921

[1] Desk Review Editor: Lia Seixas

Brasil

Brasil

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ABSTRACT